102 results for “AX”

Decompose, Estimate, Select is a coverage-aware retrieval pipeline for long-form RAG. It splits a query into subquestions, ranks candidates with a coverage-trained bi-encoder, predicts a document-by-subquestion coverage matrix with a cross-encoder, and picks documents by greedy maximum-marginal coverage. On TREC RAG 2024, α-nDCG@10 rises from 0.625 to 0.702 (gold-nugget bound 0.718). A grounded self-feedback loop raises V_strict by 0.086 over two passes.

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

M-PFT is an angular-margin method for prompt tuning of vision-language models that fixes base-to-novel dual miscalibration (underconfidence on base classes, overconfidence on novel classes). It adds two losses: minimizing the maximum cosine similarity between tunable class embeddings, and minimizing angular distance to each zero-shot embedding. Tests on 9 prompt-tuning methods and 15 datasets show lower novel-class calibration error at comparable accuracy and negligible overhead.

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

AxisQ is a post-training quantization framework for camouflaged object detection (COD) that allocates activation bits by feature depth. It uses the channel domain for shallow features and the local-frequency domain for deep features, under a fixed per-layer average bit budget. It adds a boundary-aware Hessian approximation for reconstruction. On CAMO at W2A2, weighted F-measure improves 12.7% for CFRN and 33.3% for CamoFormer-P over the strongest baselines.

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

Measurement study of 23,102 third-party Discord bots and 183,142 user reviews (2021-2026, 49 languages). A fine-tuned DeBERTa classifier plus manual validation yields 8,832 security and privacy reviews, mapped to a 31-theme taxonomy; Data Sharing (30.5%) and Data Collection (16.2%) dominate. Permission count and admin status correlate only weakly with user concerns. The authors build BotLens, an MCP-server auditing framework with six tools.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🛡️ Security

Philippe Gaborit proves that maximum-likelihood decoding of Gabidulin codes is NP-hard even for dimension one: deciding rank distance at most n-2 from the code {λg} is NP-complete. The reduction is deterministic, so Rank Syndrome Decoding and Rank Minimum Distance for F_{q^m}-linear codes are NP-complete under deterministic reductions, where the earlier reduction was randomized. It goes through a new pivot problem, Twisted Square Membership.

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

Falko Strenzke's ePrint 2026/2635 presents a multi-target second-preimage attack on message hashing in HSS/LMS and XMSS with SHA-256 and SHA-256/192. It herds chaining states from many collected signatures into one state, defeating the key and leaf-index prefix. With 2^64 signatures, SHA-256 cost falls from 2^256 to about 2^166 (2^156 at maximal message length). SHAKE256 parameter sets are unaffected.

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

Sliding Networks is a family of residual network structures with a joint width and depth scaling recipe that keeps both locally maximal feature learning and feature diversity in the residual stream. The key quantity is the ratio N_flat/(N_sharp L) tending to a constant alpha, and Half Copy correlated initialization can relax the shape constraints. Residual-MLP experiments support an SDE limit, but language-model experiments show performance differences under regrouping at fixed parameter count.

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

Persistent Closed-Form Routing (PCR) is a plug-and-play method for multivariate time-series forecasting. It alternates exact conditional estimation of channel-specific mixture weights with gradient training of low-rank residual experts around a frozen pretrained forecaster. Across eleven datasets and three backbones, PCR-K4 cuts dataset-balanced MSE by 3.86-14.25% and beats competing specialization methods with fewer trainable parameters. The paper also introduces GalaxyTS, a synthetic dataset with exact shared and channel-specific references.

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

RALE (Representation-Aware Logical Ensemble) is a method for detecting vulnerabilities in C source code with LLMs trained on different code representations: raw source, Abstract Syntax Trees and Control Flow Graphs. The authors show the representations suit different vulnerability classes, and combine the models with explicit logical rules and majority voting. Single-representation models reach up to 73% F1, while the ensemble reaches 78%.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🛡️ Security

RALE (Representation-Aware Logical Ensemble) is a method for LLM-based vulnerability detection in C code. The authors train LLMs on raw source code, Abstract Syntax Trees and Control Flow Graphs, and find each representation works best for certain vulnerability classes. Combining the models with explicit logical rules and majority voting raises F1 from at most 73% for a single model to 78%, with gains in accuracy, precision and recall.

arxiv.orgpaperAdded 28 Oct 2026, published 24 Oct 2026🧠 AI

SCI-AGENT-EXAM is a workflow-oriented benchmark of 120 expert-curated tasks across 11 scientific fields and 7 task types from the Scientific Workflow Task Taxonomy (SWTT). Tasks need both executable and textual artifacts, scored by programmatic checks plus an expert-calibrated LLM judge. Ten frontier agents were tested: GPT-6 Astra scored highest at 80.9, but only 26.0% of its runs passed the 0.95 success threshold.

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

IFC-MCQ is a method that automatically generates multiple-choice benchmarks from Industry Foundation Classes (IFC) building-model files, so answers can be graded by exact option-set match instead of human or LLM review. Its taxonomy has 15 categories and 36 subtypes in four groups, with answer keys from the file and distractors from file values or rules. Two agentic setups, tool-based and code-based retrieval, showed category-specific failures and variation by IFC model and interface.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

Paper on prompt injection delivered through forensic evidence, where the seized device's contents are attacker-controlled and the examiner must read them. It extends the anti-forensic model to cover manipulation of the reasoning layer and gives a taxonomy (objective, technique, payload location, concealment). Three proof-of-concept attacks on an LLM-assisted analysis pipeline change a reported conclusion, write attacker-chosen content to the analyst workstation, and trigger outbound network traffic. Mitigations are proposed.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

Monitorability Is Trainable proposes an unsupervised post-training objective that maximizes mutual information between a model's chain of thought and its output. On three model organisms with degraded CoT (from compression, monitor optimization, and synthetic-document generalization), it recovers most or all lost monitorability. On undegraded policies it improves CoT monitor and activation probe prediction of unsafe behavior and raises consequential reasoning.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

SoK on design-level backdoors in block ciphers. It places four published designs (Rijmen and Preneel, Paterson, Bannier and Filiol, Peyrin and Wang) in one taxonomy and gives a sound algorithm that recovers a partition trapdoor from S-box tables. It attacks KG-DES, BEA-1 and LowMC-M: KG-DES key recovery takes minutes, and the LowMC-M MALICIOUS backdoor yields the key from 35 to 409 queries. Trapdoors hidden in linear layers leave no trace, so the authors recommend deriving constants verifiably from a public seed.

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

Protection Is Not One Event is a survey and human-coded evidence map of 118 RAG defense papers, grouped by measured endpoint into a seven-family taxonomy (kappa=0.867 across 32 annotators). It finds that only 17.8% of studies use content disclosure as their primary endpoint, and that most evaluations use general QA data and non-adaptive attacks. In a testbed on Qwen3-8B, an 8-gram guard cut copying to 0.0%, yet a three-query adaptive attacker still recovered 48.3% of hidden facts. Taxonomy, dataset, coding protocols and testbed are released.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🧠 AI

Survey and human-coded evidence map of 118 RAG defense papers, organized by measured endpoint across a seven-family taxonomy (content disclosure, formal privacy proxies, access control, corpus-use detection, pipeline integrity). Only 17.8% of studies use disclosure or reconstruction as the primary endpoint, and most use non-adaptive attacks on general QA data. A testbed shows a normalized 8-gram guard cuts 20-token copying to 0.0%, yet a three-query adaptive attacker recovers 48.3% of hidden facts on Qwen3-8B.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

OpenVLHarness is a multimodal reasoning harness for MLLMs built on a capability layer (task-level tool interfaces plus evidence rendering) and persistent multimodal memory that stores images, structured data and web references as addressable artifacts. A coding interface runs generated programs over stored data. Across 22 benchmarks and eight backbones it adds 9.7-12.6 points over no-tool baselines, and GPT-6 Luna at medium effort beats Codex and Visual Sketchpad at maximum effort using at most 37% of their tokens.

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

The paper introduces PhysRSI-eval, a five-axis metric for physical recursive self-improvement: first-attempt performance, efficiency, success, budget and improvement from failure. Several VLMs are tested on 18 manipulation tasks under a fixed interaction budget. It proposes the Physical Barrel Hypothesis and studies observational scaling. Workspace interventions show that removing history lowers late-round success, while frozen workspaces still help on unseen layouts.

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

ASTOR is an inference-time framework that has an LLM map a natural-language decision problem to a grammar-guided abstract syntax tree, then deterministically compiles it into executable optimization code. The paper also introduces SoftOR, a benchmark of natural-language problems with soft constraints. On SoftOR, ASTOR has the best aggregate accuracy among comparable single-request and multi-agent LLM methods, and it stays competitive on hard-constraint-only autoformulation benchmarks.

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

This paper presents a hybrid framework for axial compressor flow that couples a Multi-Fidelity Ensemble Kalman Filter (MFEnKF) with machine learning. The filter merges PIV experiments with steady RANS and unsteady hybrid RANS/LES simulations. A neural network trained on the enriched data predicts Navier-Stokes volume forcing terms from velocity, velocity gradients and turbulent viscosity. A Mixture of Experts fixes local errors near blade tips, giving lower experimental discrepancy than baseline RANS.

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

"How employment is changing at Anthropic" is an October 2026 technical report by Nathan Wilmers, Regina Catipon, Maxim Massenkoff and Peter McCrory. It uses internal employment, agent telemetry, output, survey, calendar and hiring data plus 17 interviews. Junior technical hiring fell from 27% to 9%, agent hours reached six times employee hours by August 2026, and adopters' output rose. Employees increasingly do meta-work managing agents.

anthropic.compaperAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

PIAtlas is a taxonomy of prompt injection attacks organized by how the injected instruction subverts the model, with six mechanism classes as roots of a technique and sub-technique hierarchy. The authors release PICorpus, a license-clean collection of public prompt-injection datasets, and map thousands of records to PIAtlas under a human-verified protocol. Results show attacks combine several techniques from few mechanisms, agent benchmarks cover only part of the space, and adaptive attackers favor mechanisms that curated benchmarks underrepresent.

arxiv.orgpaperAdded 21 Oct 2026, published 18 Oct 2026🛡️ Security

OpenGCUA, from Alibaba's MAI-UI team, is an open stack for generalist computer-use agents (GCUAs), where the model chooses how to observe, act and organize work across GUI, accessibility-tree, API and shell interfaces. Its design combines code execution (GUI actions written as programs with loops and branches) with harness-to-model decision transfer. On OSWorld-v2 it raises mean partial score by +19.6 points across eight models, including GPT-6 Astra and Claude Opus 5, and cuts reported steps per task by 53%. The release includes the harness, prompts, evaluation trajectories, a macOS real-device harness, a cross-device SDK and an interactive lab. It also shows large efficiency gaps between models: Qwen3.8-Max used 19.4x the reasoning tokens of GPT-6 Astra on real macOS tasks.

huggingface.copaperAdded 17 Oct 2026🧠 AI

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

This paper shows that training-free relaxed (lossy) verification in speculative decoding causes length inflation: completions get longer, mostly through extended reasoning traces, which offsets gains in accepted tokens per round. Across twelve target-drafter pairs, relaxed rules accept 1.34x as many tokens per round but produce 1.54x longer completions. Rules that cut verifier rounds usually lose substantial accuracy, so the authors argue completion length and verifier rounds must be reported.

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

ePrint paper presenting the first optimized implementation of the BF-max bit-flipping decoder for the code-based KEM BIKE, combined with other decoders to trade off decoding failure rate, runtime, public-key and ciphertext size in ephemeral-key settings. It cuts public-key and ciphertext sizes by 7.8% versus LeanBIKE at similar speed and DFR, and gives a combined size 22% below BIKE and 2.79x below HQC. It shows BIKE can fit constrained protocols such as IKEv2, which matters for practical post-quantum migration.

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

Predictable Scaling (PS) is a framework that predicts full-scale recommendation model performance from scaled-down proxies. It builds proxies with composable scaling functions along separate resource axes, then projects metrics to target scale by curve fitting with calibrated uncertainty. Of three predictors, even the raw proxy falls within repeated-run noise, and a Gaussian Process gives 15% lower relative MAE. On a task-specific ranker and a foundation ranker, PS ranks hypotheses correctly at 79-88% compute reduction.

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

This paper analyses software supply chain (SWSC) attacks against VLSI design tools. It empirically studies the software composition, security features and tool interactions of VLSI suites, then builds a taxonomy linking SWSC attacks to VLSI attacks, design-flow stages and security outcomes. The taxonomy yields 717 possible new attacks, 60 of them high feasibility, with a threat model, a defense assessment and case studies on an open-source VLSI flow.

arxiv.orgpaperAdded 20 Oct 2026, published 15 Oct 2026🛡️ Security

Introduces Librarian Agents, where a builder agent turns raw unstructured documents into a hierarchical filesystem of synthetic files and reference links, forming a workspace for a downstream answering agent. Workspace construction is cast as a Markov decision process and the builder policy is trained with reinforcement learning to maximize downstream answering accuracy. It beats the strongest retrieval and precomputation baselines by 33 points on BrowseComp-Plus, amortizes construction cost over repeated queries, and generalizes zero-shot to BRIGHT with a 31-point average gain.

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

The paper fine-tunes a pretrained CNN wind-field super-resolution model with differentiable topology-inspired auxiliary losses on speed, gradient magnitude, soft high-speed level sets and local maxima. It also uses fixed-pair supervision at ground-truth persistence-pair endpoints located offline with the Topology ToolKit. A 16-configuration loss study on a fixed 168-field benchmark finds that gradient supervision gives the strongest persistence-diagram improvement, while fixed-pair supervision gives the most consistent joint improvement in persistence-diagram and merge-tree distances. The combined objective cuts bottleneck, W2,∞ and W2,2 distances by 31%, 25% and 27% relative to the pretrained CNN, at a cost of about half of standard fine-tuning's PSNR gain. It also shows that pointwise metrics and topological distances rank CNN and GAN models differently.

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

Proposes EdgeFlow, a hybrid KV cache migration scheme for distributed LLM inference across heterogeneous edge servers in Collaborative Edge Computing. It combines direct KV cache transmission with activation-based recomputation, overlapping computation and communication to cut migration time when servers disconnect or overload. The authors formulate a multi-node KV migration optimization problem and give a 2-approximation algorithm built on improved LP relaxation and bipartite graph techniques. Experiments on a real distributed LLM inference platform show up to 45% lower migration time.

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

Proposes a modified Physics-informed DeepONet (PI-DeepONet) architecture that removes redundancy in how the Branch network processes input functions across collocation points. It is mathematically equivalent to the original but uses smaller input tensors and fewer Branch evaluations. The authors derive training-time scaling laws, then test on 1D, 2D and 3D linear advection problems. Accuracy matches the original, the maximum speed-up is 3.75× in 2D, and a 3D model trains on an NVIDIA A100 only with the new formulation, because the original runs out of memory.

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

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) across multi-turn, tool-mediated dialogue. It formalizes auditing as a two-player Markov game with a composite reward for violation discovery, plausibility, progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization. Trained auditors find broader, higher-yield violations than zero-shot adversaries, even when much smaller than the target agent, positioning behavioral auditing as a distinct axis next to capability and safety benchmarks.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) over multi-turn dialogue. It formalizes auditing as a two-player Markov game with a composite reward balancing violation discovery, behavioral plausibility, conversation progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization in a controlled setting. Trained auditors find broader, higher-yield violations than same-architecture zero-shot adversaries, even when much smaller than the target agent. This positions multi-turn behavioral auditing as a distinct evaluation axis alongside capability and safety benchmarks.

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

INFUSE is an end-to-end benchmark for indirect prompt injection (IPI) across long-horizon agent workflows (shell commands, code changes, dependency installs, remote services). It has 40 realistic tasks and a taxonomy of 56 attack scenarios in five threat categories, with attacks injected via interaction surfaces encountered mid-task. Across 19 LLMs (GPT, Claude, Gemini, Llama, Gemma, Qwen) and 41,824 adversarial trajectories, mean attack success was 19.6% (LLM-judged) and 15.4% (heuristic), per-model 3.2–41.9%; guard models gave limited protection, while prompt-level hardening cut mean ASR from 58.2% to 14.3%.

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

Proposes a cost model and metric for an adversary who collects side-channel traces classically and finishes the key search on a quantum computer. Shows any quantum algorithm needs Omega(sqrt(r)) cipher evaluations for key rank r, and that the quadratic advantage holds only up to a rank bound set by the NIST MAXDEPTH budget (about 2^58.6 for AES-128 at D=2^40). Defines a quantum-aware key rank computable from existing rank estimators, with simulated-leakage experiments showing a depth-limited quantum adversary is worth about 22 traces at SNR 0.125.

arxiv.orgpaperAdded 16 Oct 2026, published 14 Oct 2026🛡️ Security

Proposes RTL-level architectural changes to general-purpose processors that remove the root causes of micro-architectural leakage, so masked cryptographic software runs securely under the Robust but Relaxed (RR) d-probing model without security-aware compilers or hardware-specific software workarounds. Each countermeasure is formally proven secure and integrated into the Ibex RISC-V core, which is evaluated by tool-assisted leakage assessment of the full netlist and by physical measurements on an FPGA.

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

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

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

Introduces Multi-Stream Attention Residuals (MSAR), which extends Attention Residuals (AttnRes, single-stream softmax attention over network depth) to multi-head depth attention. Heads get diverse views through multi-scale causal convolutions over sequence context, and the retrieved streams are merged by lightweight adaptive mixing into a single-stream layer input, so no persistent multi-stream storage or communication is needed. It beats standard residuals, manifold-constrained Hyper-Connections (mHC) and AttnRes on 12 downstream benchmarks at 18B and 28B MoE scales, with an iso-loss compute speedup of 1.58x versus 1.35x for AttnRes and 1.30x for mHC.

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

Proves that the nonce-based MACs Encrypted Wegman-Carter (EWC) and EWCDMD (the dual of EWCDM) are unforgeable beyond the birthday bound. With an n-bit block cipher and an O(2^-n)-AXU hash, both are secure up to 2^{5n/6} MAC queries and nearly 2^n verification queries in the nonce-respecting setting. This settles an open question left by earlier birthday distinguishing attacks (Cogliati–Seurin on EWC, Nandi on EWCDMD), which never yielded forgeries. The proofs analyze forgery events directly through auxiliary experiments. The EWC proof adds conditional permutation analysis and collision multiplicity bounds with KL divergence, and the EWCDMD proof uses Hellinger distance and Fourier analysis.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

The paper presents RM-KLPT, an algorithm that solves the algebraic pathfinding problem in isogeny graphs of superspecial abelian surfaces with maximal real multiplication. It does this by solving a norm equation in ideals of a quaternion algebra over a real quadratic field. The authors implement it and revisit the RM-CGL hash function of Charles, Goren and Lauter. They show that if the initial surface's algebraic description is known, RM-KLPT gives a second-preimage attack on the hash. Paths are much shorter (~p^3) than with KLPT^2, because a higher-dimensional Deuring correspondence exists in the RM setting. The paper also discusses cryptographic constructions that could be built on RM-KLPT.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

A narrative review in HCI arguing that the single-voice chatbot interface causes three harms: cognitive offloading, ideological narrowing of user beliefs, and epistemic compression of cultural and value diversity. It proposes multi-persona interfaces, which show several personas to the user at once, as a low-cost fix. The design draws on game design and participatory simulation (magic circle, role differentiation, persona embodiment). It offers a taxonomy for designing such interfaces and calls for empirical studies that link technical pluralistic alignment methods to real human-AI interaction.

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

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

Evaluates delivering security requirements from a governed security-by-design knowledge base (SbD-ToE) to a code-generating LLM through an MCP server at generation time. On DualGauge (59 Python tasks) the share passing all security tests rose from 44.1% to 78.0%; on BaxBench (28 backend scenarios) the share of functionally correct solutions with no successful exploit rose from 65% to 86%, matching the authors' Oracle Security Reminder (85%). The joint secure-and-functional metric did not improve significantly, since stricter code failed functional tests that fix unstated values. The study used one model and one generation per task, and it proposes measuring conformance with applicable requirements rather than only detecting known weaknesses.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

The preprint tests whether security requirements from a governed security-by-design knowledge base (SbD-ToE), delivered to a code generator through a Model Context Protocol server at generation time, make AI-written code more secure. On DualGauge (59 Python tasks, LLM-judge scoring) the share of tasks passing all security tests rose from 44.1% to 78.0%. On BaxBench (28 backend scenarios with real exploits in containers) the share of functionally correct solutions with no successful exploit rose from 65% to 86%, comparable to the authors' Oracle Security Reminder (85%). The pre-specified joint secure-and-functional metric did not improve significantly, and the stricter code failed functional tests that fix values the task never states. The study used one model and one generation per task. The authors argue that current benchmarks only detect the weaknesses their evaluators were built for, and propose measuring conformance with the requirements that apply.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🧠 AI

SLDR is a post-fine-tuning defense against malicious fine-tuning-as-a-service attacks, where an attacker fine-tunes an aligned LLM on a legitimate-looking task to covertly erode its refusal behavior. The method trains a LoRA "recovery" adapter only on the layers found to have maximal and minimal signed safety sensitivity, and uses representation-based dynamic routing at inference time to activate that adapter only for malicious queries, leaving benign task performance untouched. On Llama3.1 fine-tuned for SST2, it cuts the average harmful-output score from 11.54 to 0.08 while keeping downstream accuracy, and stays near-zero even when up to 90% of the fine-tuning data is poisoned.

arxiv.orgpaperAdded 9 Oct 2026, published 7 Oct 2026🛡️ Security

Krenn, Lesaignoux, Mir and Spini (AIT Austrian Institute of Technology) propose an "issuer inspection" mechanism for issuer-hiding attribute-based credentials (ABCs): an inspection authority can release time-bound keys that let verifiers locally flag presentations tied to a specific compromised issuer without unmasking other issuers or periods. They formalize the syntax/security model, give a generic construction, and instantiate it concretely using Groth's randomizable structure-preserving signatures, Schnorr-style proofs, and an adapted Gentry anonymous identity-based encryption scheme, showing the overhead stays practical. This addresses the accountability gap introduced when issuer-hiding ABCs conceal which authority issued a credential during verification.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🛡️ Security

TR-PTQ is a post-training quantization method for transformers that identifies learned LayerNorm scale parameters and GELU's compounded approximations (not SoftMax) as the main sources of accuracy loss when quantizing, contrary to common assumptions about numerical precision. It introduces a unified integer-only formulation using shared Taylor Region exponential/logarithm primitives so division and square roots can run entirely in the log-domain with standard integer arithmetic, paired with a calibration-free, outlier-aware LayerNorm optimization, eliminating the need for floating-point hardware for nonlinearities while keeping accuracy degradation under 1.5% across vision and language benchmarks.

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

WorldSolver is a benchmark of 168 physics-simulation coding tasks drawn from 61 classic computer-graphics papers across 7 physical domains, each giving an LLM agent a fixed code scaffold and asking it to implement the missing solver. Agents are scored on three axes — Execution Checks, Visual Fidelity, and Physical Plausibility — and frontier agents GPT-5.6 Sol and Claude Opus 5 top out at only 48.7% and 46.7% overall, showing current agents struggle to produce executable, visually and physically correct simulation code.

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

Lachesis is a lifetime-aware KV cache placement layer for LLM agent serving that sits between the agent harness and the serving engine, deciding whether to write each KV cache segment to HBM or high-bandwidth flash (HBF) based on its expected lifetime. The authors identify temporal, structural, and inter-worker axes along which KV cache lifetime diverges in agentic workloads, and show in trace-driven simulation that lifetime-aware placement extends HBF's write-endurance-limited lifetime by 1.19–3.13x over naive HBM-first placement, reaching 3.3–12.2 device-years. This matters for serving infrastructure as agentic workloads with long-lived, multi-agent context accumulation push memory capacity demands beyond what HBM alone can economically provide.

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

This arXiv methodology paper extends empirical Bayes g-modeling, which estimates a latent prior over regression coefficients from data, to the case where the entire prior distribution depends on covariates. The authors show the unrestricted nonparametric MLE (NPMLE) of this covariate-dependent prior is ill-posed, and resolve it by introducing an "infinite Mixture-of-Expertizing" class of conditional priors, proving that the resulting NPMLE is exactly a softmax-gated Mixture of Experts whose number of experts is determined by the data rather than fixed in advance. They give two exemplar-based estimation algorithms with automatic expert selection, a post-hoc aggregation step for interpretability, and theoretical results (Lipschitz continuity of conditional priors, universal approximation of continuous gating functions by aggregated softmax gates), validated on synthetic and real datasets.

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

BRANCH-MoE proposes routing experts in a Mixture-of-Experts layer through a binary decision tree of depth log2(E), where each internal node's branching probability is centered on an exponential-moving-average of arrival-weighted scores, avoiding the need for an auxiliary load-balancing loss. The paper proves the EMA mechanism prevents routing-mass collapse under log-concave arrival distributions, links expert execution frequency to stochastic-gradient convergence rate, and shows that tree-prefix device assignment bounds cross-device communication. It is benchmarked against Switch softmax routing, DeepSeek-V3 dynamic-bias routing, Skywork logit-normalized routing, and hash routing on Criteo CTR, Forest Covertype, HIGGS, and YearPredictionMSD with E=16 experts and top-4 routing.

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

This paper trains matched 300M and 1B parameter top-2 mixture-of-experts language models using softmax, 1.5-entmax, sparsemax and 2-normmax routing functions and measures how much probability mass each discards and whether tokens are dropped from selected experts. It finds routers co-adapt their learned score distributions to each map's fixed dropping threshold — entmax learns tighter score spreads that avoid dropping experts, while normmax routes 21% of tokens to a single expert — and that while no sparse map beats softmax on validation loss, sparse-trained models (especially sparsemax) are far more robust to running with a larger K at inference time.

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

SatBleed is an arXiv paper presenting the first in-depth security evaluation of commercial off-the-shelf (COTS) Communication Subsystem (COM) modules used in small satellites. The authors build a threat taxonomy for COM-targeting attacks, analyze representative COM hardware from multiple vendors, and uncover severe firmware, protocol, and architectural vulnerabilities. By correlating findings with open-source telemetry data, they identify at least 28 vulnerable missions currently in orbit that are susceptible to hostile takeover via their command-and-control link.

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

HyBind is an intraprocedural Python static-analysis framework that traces cryptographic component secrets through code to determine whether post-quantum/traditional (PQ/T) hybrid key-derivation and signature-verification logic actually requires both algorithms to succeed, rather than silently accepting a result from only one. It uses registered API contracts and function-return/execution-effect checks to distinguish real AND-logic hybrids from presence-based heuristics that wrongly treat joint and single-algorithm contributions as equivalent. The authors validate it on 50 controlled fixtures and 33 programs (15 using native ML-KEM/X25519 and ML-DSA/Ed25519 APIs), showing relaxed checks can misclassify traditional-only results as hybrid.

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

An IACR eprint paper evaluating all 13-cubed ways to assign 13 post-quantum/traditional signature parameter sets across the Root, Intermediate and Leaf roles of a TLS 1.3 certificate chain, holding the algorithm set fixed and varying only placement. Across 286 three-algorithm groups, median max/min ratios reach 2.085x for TLS handshake latency, 4.310x for certificate body size and 4.551x for path-validation cost purely from role order, with Leaf placement mattering far more than Root/Intermediate swaps. It also shows that looking only at Certificate-message size can misrank candidates such as ML-DSA-65 versus SLH-DSA-SHAKE-192s once the Leaf signature in CertificateVerify and full authentication-path cost are included, arguing PQC certificate-chain design must account for role-specific cost under real network/load conditions.

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