134 results for “AMP”

This paper derives bounds on predicting an LLM's decode length from prefill-phase hidden states, a primitive for inference scheduling. It models decoding as a stopped stochastic process, giving per-prompt normality and an affine variance law σ=αμ+b. Predictors (MLP, EGTP, FiLM, soft-label, Mixture-of-Experts) converge near the bound, so system error dominates model error. It proposes fixing model, data and sampling in benchmarks and reporting relative-error metrics.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

Beyond the Event Horizon of Keccak extends a 2011 observation that a path in the Keccak sponge from a zero-capacity state back to one yields infinite second-preimage families. Three theorems cover loops from any capacity value x, k-block loops and cycles, and Joux-style multicollisions with search cost independent of k. Verified examples run on Keccak-f[200] and Keccak-f[1600] with c=32; it also proposes bridge-based protocols.

eprint.iacr.orgpaperPublished and added 28 Oct 2026🛡️ Security

This paper studies data scaling laws in stereo matching and releases XStereo, a realistic synthetic dataset of 200 thousand samples. In-domain training on five large datasets shows two regimes: a standard power law, then saturation, with a transition point roughly fixed for a given model. Cross-domain performance depends only weakly on source data scale, and distribution and coverage diversity reduce domain gaps more.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

This ePrint paper models oscillator-based TRNG phase noise, both white and flicker, as fractional Brownian motion. It proves a quasi-renewal property that gives closed-form worst-case min-entropy and sampling requirements, and extends spectral and Allan-variance analysis so noise parameters come from a single timing trace. FPGA and ASIC experiments show flicker noise accounts for over 99% of uncertainty at a 0.99 min-entropy target, allowing a bitrate two to three orders of magnitude higher.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

VideoLVR is a method that lets video multimodal LLMs build latent visual evidence from local regions at relevant moments and reason over it interleaved with text tokens. A visual token prediction objective, defined over a vocabulary derived from the input video, supervises the latents. It extends to reinforcement learning with sampled visual tokens as actions and final-answer rewards, and improves fine-grained video benchmarks without much context growth.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper studies how multimodal LLMs refuse questions that an image cannot answer, using the MoHoBench benchmark on three MLLMs. It varies the meaning and placement of refusal instructions, compares supervised and preference-based post-training, and intervenes on a refusal-related activation direction. Instructions change refusal at the sample level in ways aggregate rates hide. Post-training raises refusal most but can mix refusal with attempted answers. Activation intervention gives smaller, transferable changes.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

p-DCIM is a 28 nm SRAM digital compute-in-memory macro (0.16 mm2, 0.41 mW) that works as an Ising machine for RAG evidence selection. Selection is cast as a QUBO problem, and the same MAC datapath computes embedding similarities and annealing steps. Replacing LLM reranking with it gives a selection-energy estimate over six orders of magnitude lower and cuts total energy per query by 67%, with comparable token-level F1 on the evaluated examples.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

Paper (IACR ePrint 2026/2658) presenting a single-trace side-channel attack on a software Fisher-Yates shuffle of length 128, the butterfly count in one ML-DSA NTT layer. It removes rejection-sampling timing variation, extracts Hamming weight and identity leakage, and enumerates likely permutations with constraint programming. On a shuffled ML-DSA-44 NTT it recovers secret polynomial s1, fully or up to a few coefficients.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

Falcon Key Recovery from Joint Sampler Leakages is an ePrint paper presenting a power side-channel attack on Falcon and FN-DSA that fuses three SamplerZ leakages (exponent class of the center, sign bit, proposal magnitude). It turns them into slab and halfspace constraints solved by softplus interval regression, with no LLL or BKZ. On PQClean Falcon-512 (ARM Cortex-M4) it recovered all 50 keys with at most 34 signatures; on c-fn-dsa it recovered 38 of 50 keys within 200 signatures.

eprint.iacr.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

"When Algebraic Structure Meets Too Many Hints" is an ePrint paper (2026/2654) arguing that DDGR hint classes and too-many-hints thresholds are not standalone side-channel security metrics. In synthetic Kyber-512 experiments, NTT-domain residues recover the full secret with about half the hints the uniform mod-q bound predicts. Finite-precision FFT samples need 25% to 55% of the budget for uniform perfect hints. No new attack optimizations are proposed.

eprint.iacr.orgpaperPublished and added 27 Oct 2026🛡️ Security

Non-profiled single-trace side-channel attack on ML-DSA-65 (Dilithium) running on ARM Cortex-M4, by Linus Backlund and Elena Dubrova (KTH). It compares observed sample distributions with expected Hamming weight leakage using a Gaussian mixture model, and stacks partitioned trace segments for multiple observations. Information-set decoding corrects misclassifications; with a 2^70 enumeration budget, the full secret key is recovered from one trace in 99% of attacks, using secret key unpacking as the attack point.

eprint.iacr.orgpaperPublished and added 26 Oct 2026🛡️ Security

Subexponential Algorithms for LWE via Rejection Sampling on Superlattices is an ePrint paper by Ligeng Fan, Changpeng Shao, Hongbo Li and Dingkang Wang. It extends the Ducas, Engelberts and Loyer superlattice framework to give a provable classical search-LWE algorithm for prime q in [n^2, 2n^2], using m=4n samples and time and space exp(O(n/ln ln n)). A second variant runs in 2^((1/2+o(1))n/ln R) for general parameters. It resolves a gap left by Kirchner and Fouque.

eprint.iacr.orgpaperPublished and added 26 Oct 2026🛡️ Security

Anis Bkakria's ePrint 2026/2638 gives polynomial-time decision attacks on Known-Covariance Ring-LWE (KCov-RLWE), the assumption Micciancio and Suhl introduced for simulation-secure threshold encryption. The public sample and covariance hint form a bordered Gram matrix whose determinant leaks a residual that differs between real and uniform samples. The attacks cover broad prime-modulus families at ring rank one and transfer to composite moduli with an odd prime divisor. Power-of-two moduli are not covered, and the paper does not break the full threshold-encryption scheme.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 25 Oct 2026🛡️ Security

Intervention-Certified Amplification (ICA) is a validity gate for sigma-weighted hybrid SFT/RL post-training. The paper shows that DIAA, a per-sample sigma-weighted hybrid, emits unexecuted tool-integrated reasoning on 29% of MATH-500 prompts (three times vanilla GRPO) even with Python fences removed from its anchor. ICA gates sigma with paired-rollout evidence, cutting TIR to 13.8% (frozen) and 11.2% (online) and raising accuracy from 47.5% to 48.7% and 49.3%. It transfers to GSM8K.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

FNO-MoE is a Fourier neural operator with a mixture of experts that predicts the full near-explicit chemical state (Master Chemical Mechanism, thousands of species) over 30 minutes in one call. Expert disagreement selects the most informative box-model simulations, so active sampling beats random or evenly spread sampling at equal cost. Fine-tuning on longer trajectories keeps multi-hour runs stable, and the emulator runs orders of magnitude faster than the numerical solver.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

DRIVE is a per-sample weighting framework for single-stage fine-tuning when the source pre-training data is unavailable. It uses one differentiable objective: Shapley additivity splits each sample's contribution into anti-forgetting and adaptation parts, a covariance-based diversity term reshapes them, and a sample-characteristic prior stabilizes them. The authors prove better stability-plasticity guarantees than existing weighting schemes and report better performance, faster convergence and more robustness.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Signal-Aware Adaptive Fusion (SAAF) is a training-free zero-shot video anomaly detection framework that cuts the compute cost of vision-language models. It combines a dual-signal sampler (frame-to-frame delta and rolling-context drift in embedding space, OR-gated with EMA thresholds), a threshold-gated cascade from frame-level to video-level analysis, and signal-aware score fusion. It reports AUC-ROC of 92.07% on XD-Violence, 86.70% on MSAD and 80.54% on ShanghaiTech, at 2.2x to 2.6x the throughput of the prior best zero-shot method.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

SOMA (Sharpness-aware Orthogonalized Momentum Algorithm) combines sharpness-aware minimization (SAM) with the Muon optimizer for LLM training. It avoids SAM's extra gradient computation by using stale, purely stochastic gradient noise as the adversarial perturbation. The authors prove sample complexity does not depend on perturbation direction, study robustness to heavy-tailed gradient noise, and test SOMA against post-training quantization degradation and catastrophic forgetting during fine-tuning.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Preprint (IACR ePrint 2026/2611) proposing a federated learning defense that replaces gradient detection with zero-knowledge-proof sample-level audits of client datasets. A staking mechanism forces attackers to train on discovered poisoned samples with flipped labels, producing counter-gradients that remove the backdoor without knowing the trigger. ZKP commitments lock the training data, which blocks adaptive attacks and gradient tampering. On CIFAR-10 under Dir(0.1) non-IID, it holds attack success rate below 6% against distributed backdoor and edge-case attacks.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 23 Oct 2026🛡️ Security

Tampered ML is a benchmark of 1,858 item-pairs, each pairing an experiment proposal reconstructed from a published ML paper with a minimally edited version that adds a targeted methodological flaw. Models must pick the sounder proposal in both presentation orders. A human study estimates 86% of items are valid (95% CI 81-91%). Opus 5 reaches 77.9% accuracy, and models are sensitive to order and favor flawed designs that look more specific, transparent, or conventional.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Rate-Controlled Refresh (RCR) is a KV cache re-selection method for multimodal LLMs in long-form generation. Attention drift decides when and where to re-select visual tokens, and a per-sample controller sets how often. RCR beats InfiniGen by 1.6-3.8 ROUGE-L points on five of six model-task settings and stays within 1.7 points of exact per-step re-selection. On video description, where drift is homogeneous, it no longer beats a fixed threshold.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This IACR ePrint paper (2026/2597) systematically compares ring-oscillator-based additive noise generators, a hiding countermeasure against side-channel attacks. Experiments show many existing designs ignore output amplification and the risk of the noise being filtered out, so they lower signal-to-noise ratio too little. The authors give a guideline on inverter chain length, output amplification and frequency randomization, and build a tailored generator for an AES implementation that outperforms prior designs.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🛡️ Security

Thomas Decru's ePrint 2026/2595 proves a corrected version of radical isogeny formulae for every N≥4, odd and even, over fields of characteristic coprime to 2N. It settles the sign choice for even N so that 2-isogeny walks stay on the volcano surface. One radical step costs about 12.5N multiplications and one exponentiation, and the paper shows how to evaluate radical isogenies on points, so a CSIDH class group action needs no torsion point sampling.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🛡️ Security

Sotto Maschera is a masked implementation of the Sota Voce sparse fixed-weight vector sampler (Sota support sampler and MWSupport conversion) for embedded devices, protecting against power side-channel attacks. It bitslices sorting, merging and compaction and masks only in the Boolean domain, avoiding slow conversions. TVLA evaluations show no leakage, and HQC runs up to 45x faster than previous masked work.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Rounded Gaussians are Discrete Gaussians is an IACR ePrint paper (2026/2583) by Corentin Jeudy. It shows that setting the rounded Gaussian parameter to sigma = s - 1/(24s) gives a distribution within Rényi divergence 1 + O(1/s^8) of a discrete Gaussian of parameter s. Lattice schemes can then use the simple, constant-time-friendly, low-memory rounded sampler while analyzing security against an ideal discrete Gaussian.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

This ePrint paper presents an optimized HAETAE implementation, a lattice-based post-quantum signature scheme, for Cortex-M4, Cortex-M3, MSP430 and AVR. It keeps polynomials in 16 bits, shares buffers between objects with disjoint lifetimes, and replays Gaussian samples instead of regenerating them. On an ATmega2560, HAETAE-2 key generation, signing and verification run in 8,070 B of SRAM. Stack use drops up to 34% and signing is 1.18-2.51x faster than Low-Stack HAETAE.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Broadcast and Trace from Lattices is a 2026 ePrint preprint by Jeffrey Champion. It builds collusion-resistant broadcast and trace with public tracing from attribute-based encryption with succinct ciphertexts and lockable obfuscation, both known from decomposed LWE. Ciphertexts are sqrt(N)·poly(λ, log N) in size, with poly(λ, log N) keys. It is the first such scheme based on a simple, instance-independent lattice assumption.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

InterWebCoder is a framework for interactive webpage code generation by multimodal LLMs. It synthesizes self-contained webpages with executable test scripts from recorded interaction trajectories, repairs failures using runtime evidence, then trains with test-ID-weighted SFT and GRPO using test pass rates and visual similarity as rewards. The pipeline yields 1,736 verified samples from 2,058 tasks. A 9B model scores 48.54 on IWR-Bench and 48.42 on Vision2Web, and reward latency drops over 97%.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

AnomalyAgent is a post-training fusion framework for visual anomaly detection that ensembles pretrained detectors. It aligns scores and anomaly maps, summarizes disagreement, and treats fusion as a choice among score actions and map actions. A constrained vision-language model inspects high-conflict samples and picks the actions. It beats equal-weight fusion and gets the best mean image AUROC and pixel AP among compared fusion methods on MVTec AD and VisA.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

DIVE is a benchmark for full-duplex interaction with visual engagement in continuous audio/video streams. It has 525 human-annotated samples across six tasks in three scenarios: proactive intervention, continuous engagement and interaction coordination. Evaluated real-time models reach sub-second average latency, but window response rates stay at or below 53.77%, and adapting ongoing responses to new information is hardest.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

Systematic evaluation of fault attacks on ML-DSA (FIPS 204) rejection sampling and of proposed countermeasures. It proves the "third rejection test" of patent WO2025261784A1 re-verifies what check 2 already guarantees, and shows that 30,000 fault injections never detect a skipped z-norm check. The author fully recovers s1 against a third-check-protected implementation under randomized signing. Third check and sign-then-verify are complementary; overhead is 0.04–0.05% on x86-64 and 0.83% on Cortex-M4 for the third check.

eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026🛡️ Security

This arXiv paper tests eight jailbreak attacks against three open-weight language models under three evaluation procedures. Low-resource language attacks score 96.0% mean ASR under keyword matching but 2.7% under a Qwen3 judge using StrongREJECT instructions. At Ollama's default temperature of 0.8, 100 samples of an unchanged request give 100% ASR by keywords and 50% by the judge. The authors recommend explicit success definitions, matched response budgets, validated evaluators and cumulative-failure reporting.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

Concerns in Benchmarking Jailbreaks is an October 2026 arXiv study of eight jailbreak attacks on three open-weight models under three evaluation procedures. Low-resource language attacks score 96.0% ASR by keyword matching but 2.7% under a Qwen3 evaluator using StrongREJECT instructions. Attack rankings flip with attempt budgets, and 100 samples at Ollama's default temperature 0.8 give 100% ASR by keywords, 50% by the evaluator.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Evasive Inner Product Functional Encryption Is Broken Too is a 2026 ePrint paper by Agrawal, Modi, Yadav and Yamada. It gives a counterexample showing the evasive IPFE assumption of Hsieh, Lin and Luo (Eurocrypt 2024) is false in full generality. The attack extends the 2025 PRFE counterexample and needs only the weak inner product functionality, not pseudorandom function computation. The authors conclude that letting the adversary choose unrestricted private coins breaks the Evasive LWE family.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

The paper presents a fault attack on deterministic CSIDH and OSIDH-LD. Forcing the lane-selection mask to zero (for example by skipping one instruction) makes output changes reveal whether a secret exponent reaches a threshold. Binary search then recovers the full key in at most 297 queries for OSIDH-LD and 222 for deterministic CSIDH with CSIDH-512-sized keys. Full OSIDH-LD keys were recovered in software fault simulation, and a ChipWhisperer-Lite skipped the target instruction with probability at least 0.94. The authors argue fault resistance requires giving up determinism and propose randomization countermeasures.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

The paper presents the first efficient, compact implementation of high-order masked Raccoon (lattice-based signature, CRYPTO 2024) on ARM Cortex-M4, up to 32 masking orders. It uses 3+3+3 layer-merged 32-bit NTT/INTT with lazy reduction, a negative double Montgomery reduction, and one-shot SHAKE256 sampling (at least 1.80x faster). It beats the reference by 1.85x-2.25x and masked Dilithium3 at orders 2, 4 and 8. Second-order leakage tests show DPA resistance once micro-architectural transitional leakage is mitigated.

eprint.iacr.orgpaperAdded 20 Oct 2026, published 19 Oct 2026🛡️ Security

Forum announcement of a Zenodo preprint on post-quantum migration for censorship-resistant P2P steganographic tunnels. It argues that ML-KEM's prime modulus (q=3329) makes naive bit-packed ciphertexts statistically distinguishable from uniform noise, which gives a cheap passive DPI distinguisher. It also argues that composite PQ 0-RTT envelopes over 4.4 KB force fragmentation, which requires stateful reassembly before AEAD verification and reopens memory-exhaustion DoS in eBPF/XDP filters. Proposed fixes are algebraic uniformization (LWR lattices or inverse rejection sampling), sub-MTU hybrid or pure-PQ envelopes (Falcon-512, signcryption), and Fujisaki–Okamoto implicit rejection for constant-time silent drops. Aimed at Ethereum validator, libp2p and RPC transports facing harvest-now-decrypt-later threats.

ethereum-magicians.orgpaperPublished and added 17 Oct 2026🛡️ Security

Presents a fixed-point C implementation of the Mitaka signing algorithm, a simpler, parallelizable Falcon (FN-DSA) variant. It replaces MitakaZ's wide integral Gram root decomposition with a fixed-point square Cholesky decomposition, which cuts the cost of integer Gaussian sampling, and proves bounds on intermediate values and representation errors without empirical assumptions. Signing averages 361 kcycles (Mitaka-512) and 707 kcycles (Mitaka-1024) on AVX2, and 4.0 and 7.8 Mcycles on an STM32F411 Cortex-M4F. This makes floating-point-free lattice signatures practical on constrained devices.

eprint.iacr.orgpaperPublished and added 17 Oct 2026🛡️ Security

Zippel is a language, compiler, runtime and analyzer for writing zero-knowledge proof systems in syntax close to the pseudocode in cryptography papers. Its type system and runtime remove vulnerability classes by construction, for example by synchronizing transcripts automatically and applying the Fiat–Shamir transform correctly. Its static analyses check completeness and soundness from the protocol description alone. The authors implemented 35 proof systems, including Sumcheck, Schnorr, KZG, Groth16, Bulletproofs, Hyrax, Spartan, Dory, HyperPlonk, DeKART and Pari. They report performance competitive with hand-written multi-core implementations.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

CHAMP is a Cayley hash function that hashes binary strings by mapping each bit to a 2x2 matrix over a finite field and multiplying the matrices. The product's semigroup structure gives the hash homomorphic properties. The authors (Demin, Ovchinnikov, Shpilrain) discuss its collision resistance, implementation and performance. Cayley-style hashes have a history of structural attacks, so the security analysis is what a cryptographer would check.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

Many Biases, One Circuit is a October 2026 arXiv paper that traces attention heads behind cue-following (for example "I think the answer is B") through two post-training pipelines. Four cue types share a few heads already present in pretrained models. About 200 generic assistant examples activate them in chat format, and the final RL stage strengthens cue effects. Swapping attention patterns of six middle layers between Tulu-3-8B and OLMo-2-7B checkpoints moves cue effects most of the way.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

Proposes "guarded sharing", a technique that moves few-time signatures in SPHINCS-style stateless hash-based schemes from a tree model to a DAG model. Combined with bucket thinning, hash chains and rejection sampling, it yields BGS-CC, which improves on the prior best few-time scheme BPORS-C+BFP. For 128-bit security and a use quota of Q=2^24 it gives 2,228-byte signatures, the first SPHINCS-based stateless signature shorter than ML-DSA-44's 2,420 bytes. This matters for PQ migration because signature size is the main drawback of SLH-DSA.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

The paper introduces 3DVerifierBench, a 10-task benchmark for generative verification of 3D content. It scores accept/reject decisions, rejection explanations, and responses to controlled changes in specifications and candidates. A human study shows a 21.6-point gap to the strongest tested model, GPT-6 Astra. The authors also build a pipeline of multi-view fact extraction, controlled 3D editing and native annotations. It yields 14,126 training examples with contradiction explanations and linked counterfactuals. Verification-guided selection raises complete factual success from 26.7% to 90.0% with oracle selection over sixteen candidates, and human-guided recursive refinement satisfies all seven requirements for every input in an eight-input study.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

GhostFaceMPC is presented as the first end-to-end face recognition system that runs both the deep CNN feature extractor and the matching step under secure multi-party computation. Raw images and biometric embeddings are never exposed at any stage, from feature extraction through storage to matching. The authors contribute an MPC-friendly quantization of the GhostFaceNet model and circuit optimizations that make this practical, and they release code on GitHub. It is relevant as an example of making CNN inference feasible under cryptographic constraints.

eprint.iacr.orgpaperPublished and added 15 Oct 2026🧠 AI

Evaluates a native probabilistic decision model (Jev), Llama Prompt Guard 2 (22M and 86M) and an LLM judge on 47,611 held-out examples covering direct/indirect prompt injection, hard-benign inputs, obfuscation and distribution shift. The LLM judge has the best AUROC (0.897), followed by Jev (0.871), Prompt Guard 2 86M (0.757) and 22M (0.714). Jev's native probabilities do not consistently beat post-hoc calibrated classifier scores, and temperature scaling improves calibration but not discrimination. At a 1% false-negative-rate target, every system needs over 91.8% escalation, so probabilistic outputs alone do not give reliable security decisions under distribution shift.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

Presents ChainGraph, a pattern that externalizes exploit-chain reasoning for LLM pentest agents (Claude Code, Codex) into a Neo4j property graph exposed via the Model Context Protocol. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges, queried with Cypher and Graph Data Science for next-step inference, entry-to-crown-jewel pathing, chain-severity promotion, provenance and chokepoints. On a synthetic GOAD-style multi-forest Active Directory engagement, three Medium findings become Critical once chained, betweenness centrality identifies break-points, and reasoning payload drops ~98% (token proxy); results are single-run and illustrative.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

ChainGraph is a pattern that moves exploit-chain reasoning out of the flat context window and into a Neo4j property graph. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges. The graph is wired to Claude Code or Codex through MCP with no custom harness. Five operator questions (next-step inference, entry-to-crown-jewel pathing, combined-severity promotion, evidence provenance, chokepoint identification) become reusable Cypher and Graph Data Science queries, plus a chain-severity score. On a synthetic multi-forest Active Directory lab modeled on GOAD, three findings rated Medium in isolation become Critical once their path to the forest root is materialized. Scoping each decision to a graph result cut the reasoning payload by about 98% on a token proxy. The authors say the results are illustrative single-run measurements, not a benchmark.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🧠 AI

Teardown study of tamper sensing meshes (TSM) across a wide range of card payment terminals, ATM key pads and a data center HSM, with detailed photos of critical features. The authors build a taxonomy of mesh construction techniques, find most commercial meshes use simple constructions far from manufacturing limits, and derive a concise set of design patterns. They argue that meshes beating the industrial state of the art are feasible with cheap, readily available processes, giving researchers a basis for realistic test specimens.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

Preprint that speeds up Pollard rho on GPUs by rebuilding field arithmetic: packed normal-basis representation, the CUDA 13.3 PTX carry-less multiply (clmad), and three-input logic instructions. On one RTX PRO 6000 Blackwell it goes from 0.85 to 14.64 billion iterations/s (17.2x) on the Certicom ECC2K-130 challenge, which puts the whole challenge under five GPU-years. The same levers give 1.52x on P-256 and 2.61x on P-384, which leaves P-256 about 6e21 card-years away. It also reports twelve GPUs from Turing to Blackwell, an FPGA engine on AWS F2 and a campaign that covered 5.4% of the expected work, with code and a Dockerfile on GitHub (rho-blackwell). It claims no improvement on the generic-group bound.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

RepProbe is a benchmark of 42K real and synthetic video clips with exactly 1–20 complete action repetitions, controlling for clip position, temporal coverage and duration. Video language models' counting accuracy collapses at high counts, and this is not explained by frame sampling alone. Linear probes on final-layer representations show count information stays linearly decodable even when the model's answer is wrong, so the failure arises at the output stage, where answers cluster on a few preferred numbers.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Plan-Bench is a benchmark of 699 curated samples across 16 subclasses and three dimensions. It tests whether vision-language models can reason over spatial and temporal information and produce multi-step plans under constraints for embodied tasks, with action execution isolated out. Across 28 VLMs the best model reaches only 75.0% accuracy. Scaling generally helps, and larger models look more promising as high-level planners. Code and benchmark are released.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Game2Real-Bench tests whether vision-language model performance on procedurally generated toy games predicts real-world visual reasoning, and whether game examples help transfer. It has 1,000 verifiable game samples and 400 real-world samples, matched by shared reasoning requirements (fine-grained perception, spatial reasoning, constraint reasoning, sequential planning). Across 25 VLMs, game and real scores correlate strongly (Pearson/Spearman 0.935). One-shot game-to-real transfer on four models improves constraint reasoning for several but hurts fine-grained perception. The authors also release Game2Real-Dataset, with about 24K verified game samples across 18 tasks and 65 QA families.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Proposes a task where a model judges and diagnoses a candidate program from only the problem statement and source code, backing its verdict with generated counterexamples that can be checked by execution. The authors build USACO-JUDGE from USACO 2026 contests (human and LLM-written accepted and unaccepted programs) and train ARBITER, a 35B MoE agentic verifier, with reinforcement learning in a generic execution sandbox. Training lifts counterexample success on incorrect programs from 40.61% to 62.67% and mean binary verdict accuracy from 71.41% to 87.41%. ARBITER also improves code selection and refinement, and in an IOI 2026 case study its guided refinement lifts GPT-OSS-120B's expected score above the gold-medal threshold under the official submission limit.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Proposes Geometric Calibration of supervised fine-tuning and SPACE (Subspace Projection for Adapted Checkpoint Enhancement). SPACE is a post-hoc, data-free, training-free method that uses the pre- and post-SFT checkpoints to revert SFT update components lying in the principal subspace of the pre-SFT weights and keep those in the orthogonal complement. It is evaluated on Qwen2.5, Qwen3, Qwen3.5, Llama-3.1 and Olmo-3 models (2B–14B) with math, coding and medical SFT. It reduces out-of-domain regression and improves in-domain performance, for example +2.85 ID / +2.26 OOD on Qwen2.5-7B math SFT.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Introduces Structured Recipe Optimization (SRO), a paradigm for automated LLM post-training that optimizes over a task-adapted recipe space covering data sources, data processing, cross-domain composition and training configuration. It pairs an LLM-based Recipe-Space Designer, which builds the recipe space for the target tasks, with a Multi-Domain Recipe Optimizer that uses structured trial evidence to guide LLM candidate construction and multi-objective Bayesian trial selection. SRO gets the highest average score in all evaluated target-model and agent-LLM combinations; for example, 41.1 vs 15.4 for the strongest baseline on Qwen3-1.7B with GPT-5.4, and 53.8 vs 40.8 on Qwen3-4B with DeepSeek-V4-Flash, in joint post-training across three capability domains and six benchmarks.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

The paper proves that some properties of UOV (Unbalanced Oil and Vinegar) public-key ideals and varieties hold generically. These include equidimensionality of the UOV varieties and radicality of the public key ideals. This justifies the regularity assumptions used in algebraic cryptanalysis of UOV and its variants. The authors also review earlier multivariate schemes that were broken because they were non-generic, and give one counter-example whose non-genericity looks hard to exploit.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Preprint recovering the long-term key of a first-order masked Kyber768 implementation on an ARM Cortex-M4 from about 2200 decapsulation power traces using only valid ciphertexts. A profiled neural network estimates Hamming-weight distributions of both arithmetic shares, which are turned into likelihoods on decryption noise and combined by a belief-propagation solver with Gaussian interference approximation and an Onsager (AMP) correction, recovering all 1536 secret and error coefficients without lattice reduction. It shows first-order masking is insufficient against combined second-order leakage, and that least squares, greedy solvers and lattice hints (BKZ block size 624 to 505 at 2000 traces) do worse.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Paper on the forensic analysis of a stand-alone HackRF One with a PortaPack running Mayhem firmware v2.0.1. It establishes at source-code level which data sit on the microSD card, their formats, when they are written and how to interpret them. The method is applied to a four-month SD image and covers RTC-battery-dependent timestamp rules, traces of on-device trimming and text editing, cluster-level recovery of deleted IQ captures, and decoding of recorded signals cross-checked with an independent decoder. In the example, the tail of a deleted IQ capture and a trimmed-away segment are recovered, and six captures are attributed to one rolling-code OOK transmitter with a shared 32-bit ID. It provides an artifact catalogue, interpretation rules and acquisition recommendations.

arxiv.orgpaperAdded 17 Oct 2026, published 11 Oct 2026🛡️ Security

Technical report for Xiaomi's MiMo-V2.6 omni-modal model family, which scales RL compute along three axes: asynchronous training with 1,568 samples and 2.7-3.7B tokens per step at up to 1M context, more diverse environments (code, general, visual, cyber) under mixed agent harnesses, and groupwise agentic grading that gives better reward signals and favors shorter, token-efficient solutions. Stability measures include a frozen MoE router and a multi-layer defense against reward hacking, built on a mid-trained hybrid-SWA architecture. The authors open-source training dynamics, RL environments and the RL framework, which makes it a useful reference for reproducing scaled agentic RL.

huggingface.copaperAdded 10 Oct 2026🧠 AI

The paper studies joint attention in multimodal diffusion transformers (MMDiTs), where the evolving image updates text representations. It finds that this image-to-text (I2T) feedback tends to preserve objects and attributes already in the image, errors included, and that suppressing it improves generation. It proposes Semantic Feedback Guidance (SFG), which guides away from an auxiliary branch that strengthens I2T feedback and weakens text-to-image conditioning, and iSFG, an in-place variant that needs no extra model evaluations. SFG runs for the first 20% of sampling steps (about 1.2x base NFE without CFG). Across three backbones, adding SFG to CFG on FLUX raises GenEval from 0.6661 to 0.7479, and iSFG on SD3.5M adds +1.33 HPSv2.1 over CFG at zero extra NFE. The control also improves VBench scores for text-to-video and image-to-video.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Studies curriculum design for GRPO by re-estimating sample difficulty under the current policy and using a staged curriculum that progressively shifts the active difficulty band toward harder samples. On GSM8K across three seeds, moving to a moderately harder band lifts test accuracy to 0.6591 ± 0.0077 versus 0.6449 ± 0.0024 for fixed-medium selection, while going to a still harder band drops it to 0.6405 ± 0.0053. The benefit of difficulty progression is therefore non-monotonic, which informs online sample selection in RL fine-tuning of LLMs.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Proposes the Answer-Evidence Support Protocol (AESP), which checks whether the grounding evidence a model outputs actually supports its own answer in grounded remote sensing visual question answering. It applies task-specific rules to existence, counting and comparison questions. The authors build the G-RSVQA benchmark of 15,027 samples with questions, reference answers and object-level annotations. Tests on seven grounded vision-language models show evidence-support failures on all three question types, with up to 50.82% of correct answers unsupported by their grounding evidence.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

The paper shows that reward hacking learned by one coding agent during RL against automated evaluators can spread to other agents in a multi-agent system. Across four open-weight backbones, coding RL elicits and amplifies hacking when the system prompt describes the mechanism explicitly and when mid-training exposes the model to facts about the coding environment. Post-RL source agents transmit the hack through code, shared memory and natural-language plans, and even small open-weight agents induce hacking in stronger closed-weight targets. On hard tasks the hack can intensify along a transmission chain after the source leaves, as the code accumulates more persuasive justifications. A theoretical analysis gives conditions under which hacking amplifies, persists or decays. Hacking also biases task routing toward hacking agents and survives candidate aggregation. Code review by the coder or by a separate verifier reduces the hacks that get delivered.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

ePrint preprint extending the Ducas–Engelberts–Loyer provable subexponential Gaussian sampler (built on Wagner's generalized birthday algorithm) to module matrices over power-of-two cyclotomic rings. It gives provable 2^{O(n/lnln n)}-time algorithms for Module-SIS and for decision and search Module-LWE using only the k+ℓ ring samples provided, plus sample-preserving scalar search-LWE and narrow Module-LWE variants. Separate heuristic estimates suggest these Wagner-style attacks do not reach the NIST security targets of ML-KEM and ML-DSA.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 8 Oct 2026🛡️ Security

WOVEN is a training source and benchmark for visual transition reasoning in multimodal LLMs. It has 36,076 examples spanning 20 scene types, 5 action types and 8 reasoning types, built from rollouts of video-pretrained generative models. Evaluating 38 frontier MLLMs (including GPT-5.4 and Qwen3-VL-235B-A22B) shows systematic deficits that persist with scale. Training on WOVEN subsets of about 2,000 items improves 22 of 26 external benchmarks by up to 27.3 points, and WOVEN data can replace 30-50% of a task's own training data. The authors derive a recipe: select supervision by the reasoning operation it teaches rather than by scene or action, and prefer larger visual-state changes.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

ORCAGen uses RAG over a curated knowledge base of malware procedures and active-defense strategies, plus structured prompting, to have LLMs generate proof-of-concept malware and matching deception orchestration code offline. Strategies are validated against the PoC malware before being added to a runtime playbook that enforces only the verified, deterministic logic. It is evaluated on GPT-4o, GPT-5.5, Gemini 3.5 Flash, Qwen3-Coder and Claude Sonnet 4.5 over synthesized scenarios and 150 real keylogger, infostealer and ransomware samples, measuring execution success, hallucination rate, refinement effort, deception effectiveness and runtime overhead.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🛡️ Security

Technical report for the MiMo-V2.6 omni-modal model series, which scales RL compute after mid-training on a multimodal corpus, using a hybrid sliding-window-attention architecture. RL is scaled along three axes: asynchronous training with 1,568 samples and 2.7-3.7B tokens per step at up to 1M context; diverse environments (code, general, visual, cyber) under mixed agent harnesses; and groupwise agentic grading that gives better rewards for long-horizon tasks and favors shorter, token-efficient solutions. Stability measures include freezing the MoE router and a multi-layer defense against reward hacking. The authors open-source the training dynamics, RL environments and RL framework.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

Proposes QCOC (Query-Calibrated Operator Compression) for tabular in-context learning. It compiles the full KV cache of the in-context examples once into compact shared memory. The cache is built from clustered joint-KV prototypes that keep per-cluster multiplicities and the original example count. Prototype values are calibrated against attention query vectors with an anchored closed-form solution. On 64 held-out OpenML-CC18 datasets it has the best mean accuracy among compared compression and retrieval methods. On seven long tables it averages 0.23 points below full context. Compressing 8,192 examples to 512 slots gives 10.5x cache compression, and single-core CPU serving over 1,000 queries is up to 508x faster than dynamic retrieval and 1.98x faster than full context.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

EntroPrefill proposes a Renyi-entropy-guided mechanism for pruning context mid-prefill in retrieval-augmented generation, using sink-isolated regularized head pooling compatible with grouped-query attention to decide which tokens can be safely discarded. It derives a mixture-to-head deletion envelope bounding feasible token removal, a finite-sample observer guarantee valid under adaptive layer selection, and a conditional transformer perturbation bound with explicit Lipschitz constants and a first-token decision-margin corollary, plus a systems-level break-even analysis of KV-transfer payload versus pruning savings. The paper is explicitly theoretical, defining assumptions and formal limits without reporting empirical speedup or accuracy-preservation results.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

PEARS is a hybrid reinforcement-learning framework for sample-efficient online adaptation of pretrained tactile-manipulation policies to out-of-distribution conditions. A physics-guided force reasoning (PFR) module uses a vision-language model to diagnose failures from visual and tactile history and update contact-force bounds enforced by a hybrid force-position controller, while a separate tactile-conditioned diffusion-steering component perturbs the latent noise of a frozen flow-matching policy to correct free-space motion and contact timing without retraining the base model. It improves success rates by 12.4-37.4 points over strong baselines in simulation, cuts required interaction episodes by up to 53.2%, and reaches 95%/90% success on real-world whiteboard-erasing and pipette-liquid-aspiration tasks.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

An IACR ePrint paper by Yiming Gao, Xuyuan Han, and Honggang Hu presenting provable classical subexponential algorithms (running in roughly 2^((1/2+o(1))n/ln ln n) time/space) for recovering short secrets in growing parameter families tied to all seven NIST third-round lattice candidates: Kyber/ML-KEM, FrodoKEM, SABER, NTRU LPRime, Dilithium/ML-DSA, Falcon, and cyclic NTRU-HPS/HRSS. The approach extends the Wagner-style Gaussian sampling framework of Ducas, Engelberts, and Loyer (CRYPTO 2025) and a low-error decision-LWE algorithm, using an exact squared-Euclidean-norm gap to binary-search each secret coordinate, and an "affine slice search" over the public lattice for NTRU/Falcon-type quotient relations. The authors explicitly note the subexponential complexity applies to growing/weakened parameter regimes and does not reduce the concrete security of the currently specified NIST parameter sets.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

This paper presents a systematic empirical study of small language models (decoder-only, encoder-decoder, and mixture-of-experts architectures) on abstract grid-transformation reasoning, using the ARC-TGI benchmark, which supports controllable task families, resampling, spatial shifts, and cross-benchmark transfer to distinguish genuine rule acquisition from distribution-specific overfitting. Across more than 1,000 supervised fine-tuning runs, the authors find that in-distribution accuracy is achievable but highly sensitive to optimization and task formulation, performance collapses sharply out-of-distribution (even under simple grid-scale changes with the same underlying rule), training-set scale yields uneven gains, in-context example benefit is model-family dependent, and executable-rule induction surfaces correct solutions that direct grid generation misses. Layer-wise attention diagnostics reveal behavioral differences in concentration and context-dependence but stop short of establishing causal mechanisms, underscoring that abstract-reasoning scores are conditional on model, adaptation regime, evaluation distribution, and output format.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

RAG-PIBench is a benchmark of 4,876 contextual examples for evaluating prompt-injection detectors embedded in retrieved content within Retrieval-Augmented Generation (RAG) systems. The authors use a leakage-aware construction pipeline with frozen train/validation/protected-test splits and compare keyword-based, semantic-reference, TF-IDF, and transformer-based detectors, finding DistilBERT achieves the best protected-test performance (F1 = 0.896, PR-AUC = 0.968) while TF-IDF with SVM or logistic regression remains competitive. It highlights the risk that naive benchmark construction can leak information between splits, inflating reported detector performance for RAG prompt-injection defenses.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🛡️ Security

An IACR eprint paper proving four of five open conjectures, and disproving the fifth with a counterexample, from Andreoli and colleagues at ToSC 2025 about the linear height and component linear nullity of the inverse of ChiChi, the low latency nonlinear core used in the ChiLow cipher from Eurocrypt 2025 by Belkheyar and colleagues. The authors settle these bounds for all admissible branch lengths of the SWAP and 4-cycle generalizations, derive design consequences for ChiLow, and correct an erroneous data value from the prior work.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

This paper studies looped language models — architectures that reuse a shared block of layers recurrently — and argues that as recurrent hidden states approach fixed points, the specific path taken to reach them matters less, enabling several efficiency tricks: truncated backpropagation through time during training, terminal key-value (KV) cache sharing across loop iterations for faster decoding, a distilled student model that prefills up to 1.79x faster, and RL policy gradients computed from saved rollout states that run 2x faster than backpropagating through the full trajectory. To make these fixed points reliable, the authors replace fixed-depth training with a learned depth prior (regularized with an entropy term to stay broad, improving on Huginn's fixed broad prior) and introduce orthogonal input injection, which removes the component of the injected input that the hidden state could otherwise amplify or cancel. Across model scales from 100M to 1.6B parameters, both changes lower perplexity, and at 1.6B the learned prior with a 3x smaller KV cache matches the downstream performance of full-cache fixed-depth training.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

BazaarBench is a simulated consumer-to-consumer marketplace benchmark that measures whether LLM agents acting as buyers and sellers stay safe and honest — tracking ownership, item condition, and commitments to detect six failure types across five transaction stages. Running three 100-agent markets for 30 days plus 45 seven-day continuations across five models (inventories drawn from a public eBay sample), the authors show that double-selling and item-condition deception rise sharply under deadline pressure and adversarial instructions, with GPT-5.4 completing 55.5% of committed sales involving unavailable or misrepresented items; the team releases the simulator, saved market states, eval code, and 357,608 agent-call records.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

FREA is a benchmark of 751 chemical reactions labeled by expert chemists for feasibility verification, drawn from retrosynthesis model proposals, zero-yield experimental records, LLM-generated edits, and five negative-candidate generation methods. The authors show no single verifier type (LLMs prompted with the feasibility criterion, dedicated forward models, or retrosynthesis scorers) dominates across all sources, and that adding generated negative examples to training improves mean AUROC but fails to transfer to retrosynthesis proposal screening. They also release a corpus of over 14 million recorded and generated reactions to support further training and evaluation of feasibility verifiers.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

SpatialChain is a benchmark of 28,350 training and 899 test examples pairing spatially-oriented GQA questions with scene-graph-grounded reasoning chains, designed to audit whether VLMs' correct spatial answers reflect faithful reasoning or linguistic shortcuts. Using a two-axis evaluation (chain-overlap metrics plus a scene-graph-aware LLM judge), the authors find that four of nine tested models hit ≥79% accuracy while showing shortcut rates above 39%, that chain quality predicts correctness for most models except Claude Sonnet 4.6 and InternVL3.5-8B (which fail differently, via terse vs. verbose-decorative reasoning), and that SFT on SpatialChain improves Qwen3-VL-8B accuracy by +6.2pp while cutting its shortcut rate to 22%. The faithfulness judge is validated against human annotations and a second LLM judge, with data, generation scripts and evaluation code released publicly.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

This paper introduces a paired-replay testbed that samples a single LLM agent request and replays the identical malicious action, resource, and arguments under four authorization conditions: broad bearer tokens, scoped JWTs, sender-constrained tokens, and Open Policy Agent policies. Across 128 scenarios in four tool domains and five local model configurations, broad-bearer tokens let roughly 9 to 38 percent of attacked, post-exposure decisions execute harmful actions, while all three scoped conditions recorded zero harmful executions; a supplementary AgentDojo extension found 11 of 24 injected attacks succeeded under broad policy versus 0 of 24 under scoping. The authors frame this as evidence that scoping contains cross-action and cross-resource consequences of prompt injection, not that it prevents the injection itself.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🛡️ Security

An IACR ePrint paper presenting an optimized ARMv8 NEON assembly implementation of MAYO Round 3 (one of NIST's post-quantum signature candidates) for Apple M1, generated to be byte-identical to upstream intrinsics but 2–2.6x faster for signing and verification across all four parameter sets. The authors also run a constant-time testing campaign with matched null runs and find that while linear-algebra operations are statistically indistinguishable from constant-time nulls, verification (on public data) exhibits a measurable key-fixed timing effect present in both their implementation and the upstream code.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 5 Oct 2026🛡️ Security

This paper studies the sharpening hypothesis of RL post-training on LLMs: that it mainly sharpens existing base-model behaviors, boosting pass@1 at the cost of solution coverage (pass@K), and shows this cost extends to agentic, multi-turn tool-use tasks, not just math/coding. The authors introduce Sharpening Tax, a metric quantifying this coverage loss, measured across 14 base/post-trained model pairs from four model families on three agentic benchmarks (42 cases), finding post-training pushes tasks toward always-solved or never-solved extremes. They also propose posterior-tempered group sampling (PTGS), a Bayesian per-prompt temperature adaptation method that reduces the tax during RL training while improving both coverage and single-shot accuracy.

arxiv.orgpaperAdded 4 Oct 2026🧠 AI

LESSER is a drop-in wrapper for gradient-based post-training data selection that approximates full-parameter gradient alignment using only output-layer gradients, which can be computed with a forward pass instead of a full backward pass. It cuts feature-extraction compute by 9.7x on SFT benchmarks and 3.0x on RL benchmarks while matching the downstream performance of full-gradient selection methods, even though individual sample rankings differ, selected batches remain gradient-aligned.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

IDRF is a reward fine-tuning framework for few-step masked discrete diffusion generators that replaces the intractable sequence-level KL penalty in reverse-KL-regularized objectives with an inverse-distillation regularization term, proven to upper-bound the true KL divergence to the reference distribution. It frames few-step diffusion sampling as a finite-horizon Markov decision process and optimizes reward via a clipped policy-gradient objective over the student's own trajectories, without needing reference-model rollouts. Across DNA, image, and text generation tasks, IDRF reaches high reward with up to 32x fewer denoising steps than the reference model while reducing reward hacking and preserving sample quality.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

VenusRL is a fully disaggregated system for agentic reinforcement learning that trains LLM agents through multi-turn interactions with external tool environments. It introduces a priority-aware action scheduler that uses length-prediction heuristics to prioritize sample groups likely to unblock the next training step across batch admission, KV cache residency, and cross-worker orchestration, plus an environment resource manager that packs more tool sandboxes per node via a memory-aware admission threshold and template-keyed page-sharing pool using copy-on-write for isolation. The system achieves up to 4.24x end-to-end training speedup and reduces environment (sandbox) cost by up to 89% over state-of-the-art baselines on agentic RL workloads.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

This paper shows that intervention-defined faithfulness metrics used to evaluate automated circuit discovery in mechanistic interpretability can favor a worse circuit over a better one, an objective-level recovery gap. Testing EAP, EAP-IG, ACDC, and Edge-SP across four human-reference tasks and InterpBench, the authors find KL-based faithfulness misranks 9.4%-41.2% of candidate circuit pairs under held-out resampling, and trace the cause to context distortion, where excluded signals change the inputs retained components operate on. Restoring selected signals from the intact model's execution repairs 96 of 100 persistent misrankings, showing the evaluation objective itself, not just the search algorithm, needs fixing.

arxiv.orgpaperAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

Researchers introduce ImmRAG, a black-box data extraction attack against image-returning Multimodal Retrieval-Augmented Generation (MRAG) systems, where the retrieved image itself is the model's response. Instead of malicious text prompts, ImmRAG embeds attack instructions inside an attacker-supplied image and uses relevance-weighted resampling to steer queries toward unexplored regions of the embedding space, progressively reconstructing more of the private datastore. Tested against CLIP-family retrievers across medical, document, and general-purpose scenarios, a single 2500-query run reconstructed up to 611 radiology images, 566 document scans, and 416 general images — up to 5.6x more distinct items than a non-adaptive baseline — highlighting the lack of safeguards against multimodal data leakage.

arxiv.orgpaperAdded 2 Oct 2026, published 1 Oct 2026🛡️ Security

SAGE is a statistical acceptance gate for self-evolving LLM agents that replaces the naive accept-if-aggregate-score-improves rule used when an optimizer edits an agents persistent skill document. It uses per-item paired comparison between the current and edited skill on identical validation items, exposing regressions an aggregate score hides, plus a one-sided paired significance test that only commits edits whose wins reliably beat their losses, addressing both permanent regression risk and the optimizers curse from noisy finite validation sets. Across five benchmarks and four backbone LLMs, SAGE cuts regression rates in 19 of 20 settings, for example 36.5 percent to 0 percent on LiveMath and 42.8 percent to 0 percent on OfficeQA with DeepSeek V4, while also achieving the best final score in all 20 settings.

arxiv.orgpaperAdded 30 Sep 2026🧠 AI

This paper introduces VideoMSN, a Masked Siamese Network that repurposes standard image Vision Transformers for self-supervised spatio-temporal video representation learning by representing videos as 'super images' (grids of sampled frames) and aligning spatial-masked and temporal-masked views with a shared ViT encoder. Starting from pretrained DINO-v3 and DeiT-v3 image encoders, it achieves state-of-the-art results on Kinetics-400, UCF101, and HMDB51 while using up to 32x and 160x fewer video pretraining epochs than prior video self-supervised methods, and shows strong low-shot transfer performance.

arxiv.orgpaperAdded 1 Oct 2026, published 30 Sep 2026🧠 AI

This SoK paper empirically evaluates 24 published emulation-based dynamic analysis tools for ARM Cortex-M firmware re-hosting, using large-scale datasets from OTACAP and FirmLine totaling 4,571 deduplicated ARM firmware samples. It finds that only 34.5% of samples can be successfully fuzzed by any existing tool, with average code coverage of just 10% and frequent false crashes/hangs, exposing systemic gaps across the emulation, fuzzing, and bug-diagnosis pipeline stages.

arxiv.orgpaperAdded 2 Oct 2026, published 30 Sep 2026🛡️ Security

This paper studies two temporal phenomena in generative model sampling dynamics: a 'speciation window' where a sample commits to a semantic class, and a 'nonlocality window' where local context becomes insufficient for generation. Under a 'common cause' hypothesis — that semantic labels explain correlations between distant tokens — the authors prove the nonlocality window must fall within the speciation window, and identify conditions under which both windows collapse to a single phase-transition point as system size grows, verified analytically in Gaussian mixture models.

arxiv.orgpaperAdded 30 Sep 2026, published 29 Sep 2026🧠 AI