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huggingface.copaper🧠 AI

Your Benchmark Is an Empirical Measure Over Difficulty

The paper treats a benchmark as an empirical measure over item difficulty and formalizes two failure modes: measure mismatch (benchmark difficulty distribution differs from the intended one) and coverage gaps (uncovered difficulty regions that hide localized training progress). It proposes two diagnostic metrics with theoretical guarantees and analyzes 25 popular benchmarks across 7 domains using estimated item difficulties. It also introduces BenchFill, a difficulty-targeted item rewriting algorithm that reduces local coverage gaps.

BenchFill

evals, research· Added 31 Oct 2026

arxiv.orgpaper🧠 AI

Don't Lose the Thread: Empowering Long-Horizon LLM Agents with Cognitive Resource Self-Allocation

CORAL (Cognitive Resource Self-Allocation) is an agent-callable working-memory toolset that lets an LLM agent checkpoint task progress and verified facts, then reset its cluttered context and resume from a clean state. A Multi-episode Agentic Reinforced Policy Optimization algorithm trains the checkpointing. With an 8B model it reaches 42.8% average accuracy on GAIA, uses 6-7x fewer input tokens than a ReAct agent, and adds 8.0 points to a proprietary agent on BrowseComp.

LLMReActCORAL

Multi-episode Agentic Reinforced Policy OptimizationGAIABrowseComp

agents, research, training· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Quasi-polynomial time algorithms for block basis reduction of linear codes

Kolesnikov and Novoselov give block basis reduction (BKZ) algorithms for linear codes that need polynomial or quasi-polynomial numbers of calls to a minimum weight codeword oracle at fixed block size. The earlier Ghentiyala and Stephens-Davidowitz bounds are exponential. The paper adds a truncated BKZ and a DeepBKZ variant, with an implementation for block sizes 2 to 24 and approximation-factor estimates on random codes.

BKZDeepBKZminimum weight codeword problem+4

other· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

DICE: Decentralized Incentive-driven Federated Learning for Efficient & Scalable Customization of Pretrained Models in Web3

DICE is a blockchain-powered federated learning (BCFL) algorithm for customizing large pretrained models among untrusted Web3 participants. It combines dynamic progressive learning and model slice verification, so hardware-limited participants can train part of the model, with a tree-structured committee consensus protocol and an efficiency-driven incentive scheme to scale to more data holders. Experiments cover vision and language models.

federated learningblockchain-powered federated learningprogressive learning+1

arXiv

research, training· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Shor's algorithm compiled on cat qubits: 22 593 qubits for RSA-2048 in 3.14 days

Shor's algorithm compiled on cat qubits is an IACR ePrint (2026/2665) giving an end-to-end resource estimate on superconducting cat qubits, using repetition and LDPC cat codes. Factoring RSA-2048 needs about 23,000 cat qubits and 3.14 days, assuming phase-flip noise p_Z = 5e-4 and no bit-flips. secp256k1 ECDLP needs at most 30,000 qubits and 1 hour. A space-for-time trade runs RSA-2048 in 1.25 hours on about 611,000 qubits.

Bitcoin

Shor's algorithmRSA-2048secp256k1+5

other· Published and added 28 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Radical isogenies on every model

Radical isogenies on every model is an IACR ePrint paper by Thomas Decru (October 2026). It gives radical isogeny steps of every odd degree N directly on short Weierstrass, Montgomery, twisted Edwards, Huff, Jacobi quartic, twisted Hessian, Legendre and Jacobi intersection curves, plus Kummer lines. In Tate normal form a step drops from 12.5N to 10N multiplications. Montgomery and twisted Edwards curves cost 10.5N+O(1) per step and 2N to push a point. All algorithms are inversion-free, with exact operation counts.

CSIDH

Thomas Decruradical isogeniesVélu's formulae+1

other· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Alg\{Ossa\}: Decentralized Secure Aggregation with Rotating Committees and Eventual Delivery

AlgOssa is a decentralized secure aggregation protocol built on Ossa, a generic committee-aided, single-server construction for one-shot clients (following OPA and Willow, CRYPTO 2025). Ossa realizes secure aggregation with non-selective abort against a malicious server. AlgOssa adds Algorand-style sortition to rotate leaders and committees, bounds the chance of an honest server, and gives eventual delivery. Classical and post-quantum routes are evaluated.

Algorand

OssaAlgOssaOPA+2

other· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

When Algebraic Structure Meets Too Many Hints

"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.

Kyber-512ML-KEM

DDGRtoo-many-hintsMLWE with Hints+4

Hardware & RF· Published and added 27 Oct 2026

arxiv.orgpaper🧠 AI

Your Wish Is My Commit: Over-Compliant Refactoring in AI Coding Agents

Your Wish Is My Commit is a controlled study of over-compliant refactoring in Codex and Claude Code, run across five models, 50 algorithmic tasks and 10 object-oriented tasks. Over 3,000 turns of repeated "refactor further" prompts, no model stopped on its own: each rewrote up to 31x the original codebase before first stopping and resumed edits after declaring the task complete. The paper defines six behavioral signals for detecting over-compliance.

CodexClaude CodeLLM

arXiv

coding-agents, research, evals· Added 28 Oct 2026, published 26 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Subexponential Algorithms for LWE via Rejection Sampling on Superlattices

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.

Ducas, Engelberts and LoyerKirchner and FouqueBKW algorithm+1

other· Published and added 26 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Faster Matrix Multiplication Leads to Faster Lattice Sieving

Gao and Hu's IACR ePrint 2026/2622 shows that if the matrix multiplication exponent satisfies ω ≤ 9/4 − η, heuristic lattice sieving runs in (3/2 − δ)^(n/2+o(n)) time under the spherical heuristic from the BDGL analysis. It builds on OpenAI's recent result that ω ≤ 9/4. With known matrix multiplication algorithms, the method already gives subquadratic search inside BDGL buckets. Only the abstract is available, so concrete parameters are not shown.

BDGLspherical heuristicmatrix multiplication exponent

other· Added 26 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Tight Post-Quantum Multi-Key Security of the FX Construction

Rentaro Shiba and Tetsu Iwata prove a tight multi-key security bound for the FX construction (the DESX generalization) in the quantum ideal cipher model. Security holds up to about 2^((n+κ)/3) total classical and quantum oracle queries against fully adaptive adversaries, for any number of keyed instances μ≥1. This improves their ToSC 2026 bound and matches the offline Simon's algorithm attack in the exponent.

FX constructionDESXoffline Simon's algorithm+4

other· Added 24 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

Sharpness-aware Orthogonalized Momentum Algorithms

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.

MuonLLM

SOMAarXiv

research, training· Added 26 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

CellTrack-Bench: Microscopic Cell Tracking Benchmark for Vision-Language Models

CellTrack-Bench is a benchmark that tests vision-language models on microscopic cell tracking and compares them with specialised trackers. It has 1,537 problems covering 9,041 cell-linking decisions, drawn from 15 cell-tracking datasets across four imaging modalities, with a hard subset built from failures shared by distinct algorithmic trackers. The authors compare environment harnesses, benchmark 9 closed-source and 13 open-weight models, and analyse failure modes.

vision-language modelLLM

CellTrack-BencharXiv

evals, multimodal, research· Added 24 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Linear Cryptanalysis of TFHE-Friendly Cipher FRAST in the Average Key Setting

Linear cryptanalysis of FRAST, a TFHE-friendly stream cipher, in the average key setting. It fixes the false-positive problem left open in the earlier attack by Bak et al., showing key differences fall into five indistinguishable classes per round. The Contract algorithm recovers the master key for 10 rounds and, on weak keys of density 2^-11.8 to 2^-5.4, reaches full FRAST within the claimed security.

FRASTTFHE

Contract algorithmBak et al.linear cryptanalysis+1

other· Added 24 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

SoK: Design-Level Backdoors in Block Ciphers Constructions, Detection, and Key Recovery

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.

KG-DESBEA-1LowMC-M+2

Rijmen and PreneelPatersonBannier and Filiol+5

other· Added 24 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Table-Free Constant-Work BCH Decoding by Algebraic Index Elimination

Table-free constant-work BCH decoding by algebraic index elimination is an IACR ePrint paper (2026/2580) that replaces the table scans in Walters and Sinha Roy's constant-work BCH decoder with fixed circuits over public addresses. It proves correctness through radius t and trace regularity, removes 5.2 and 7.2 KiB of tables, and runs 35.7 to 82.4 times faster on an i7-13700. Ordinary C masks compiled to received-word-dependent branches under Apple Clang 21, so the authors use a compiler-opaque primitive and run timing screens on four platforms.

Apple ClangC

Walters and Sinha Royalgebraic index eliminationANSA-IBKEM1+3

Hardware & RF· Added 24 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Regular Syndrome Decoding Is Easier Than Previously Estimated

Preprint by Jiseung Kim, Minkyu Kim, Changmin Lee and Hochang Lee on the Regular Syndrome Decoding (RSD) problem. It presents a hybrid-algebraic attack that balances zero-coordinate guesses and syndrome-based eliminations across regular blocks. Parameter sets in Ferret, Wolverine and committed VOLE fall below 128-bit targets by up to 67 bits, and the revised Liu et al. (EUROCRYPT 2024) PCG sets cost under 2^111 field operations.

Regular Syndrome DecodingFerretWolverine+4

other· Added 21 Oct 2026, published 20 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Regular Syndrome Decoding Is Easier Than Previously Estimated

Preprint 2026/2563 presents a refined hybrid-algebraic attack on Regular Syndrome Decoding (RSD) that balances zero-coordinate guesses and syndrome-based eliminations across all regular blocks. It puts at least one parameter set each from Ferret (CCS 2020), Wolverine (IEEE S&P 2021) and committed VOLE (CCS 2025) below the 128-bit target, with losses up to 67 bits. PCG attack costs improve by up to 29.77 bits (non-binary) and 25.91 bits (binary), and the revised Liu et al. (EUROCRYPT 2024) 128-bit sets fall below 2^111 field operations.

FerretWolverine

Regular Syndrome Decodinginformation-set decodingpseudorandom correlation generator+3

other· Added 21 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Scaling Parameter-Efficient Fine-Tuning for Brain Encoding

BrainLoRA is a parameter-efficient fine-tuning method that adapts multimodal backbones of up to 104B parameters to predict neural responses. It predicts multiple timepoints in one causal pass instead of sliding windows, and a multi-LoRA kernel trains many adapters on a shared backbone. Across nine backbones it improves encoding, and a 200+ model ensemble ranks first on the Algonauts post-challenge leaderboard.

LoRAQwen3Transformer

BrainLoRAQwen3-OmniAlgonauts 2025 Challenge+1

research, training, multimodal· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🧠 AI

Analysis of an AI based code distinguisher

The paper analyses DeepDistinguisher, a transformer-like model that tells Goppa codes apart from random linear codes. Saliency maps and attention visualisation reveal a low-weight-row artifact in the random training instances that the model partly relies on. A pairwise-XOR rule inspired by minimum distance correlates strongly with the model's predictions, and the model loses most of its power on random matrices meeting both constraints. This suggests it exploits general combinatorial properties, not hidden algebraic structure.

Transformer

DeepDistinguisher

research· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Analysis of an AI based code distinguisher

Analysis of DeepDistinguisher, a transformer model that tells Goppa codes from random linear codes. Using saliency maps and attention visualization, the authors find a low-weight-row artifact in the random training instances that the model partly uses. They propose a pairwise-XOR rule on generator matrix rows that correlates strongly with the model's predictions. On random matrices meeting the minimum distance and XOR constraints, the model loses most of its accuracy, so it exploits general combinatorial properties rather than hidden algebraic structure.

DeepDistinguisherGoppa Distinguisher problemGoppa code

AI Security, other· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Provable Lattice Reduction in Nearly the Time of QR Factorization

Provable Lattice Reduction in Nearly the Time of QR Factorization (IACR ePrint 2026/2541, October 2026) presents a deterministic lattice-reduction algorithm with running time Õ(n^ω(1+log κ_col(B))), free of heuristic assumptions. Its implementation used fewer instructions than flatter (CRYPTO 2023) in all 153 runs on q-ary, NTRU-like, scaled q-ary and Coppersmith inputs. Median flatter-to-new instruction ratios are 1.55 for q-ary and 1.86 for NTRU-like inputs.

flatter

CoppersmithLLL

other· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Computing endomorphisms of superspecial principally polarized abelian surfaces in time and memory \(p^{4/5+o(1)}\)

Computing endomorphisms of superspecial principally polarized abelian surfaces is a cryptanalysis preprint by Marc Houben and Lam L. Pham, posted to IACR ePrint in October 2026. It gives an algorithm that, under a standard heuristic, finds a non-scalar polarized endomorphism in expected time and memory p^(4/5+o(1)). The abstract is the only text available, so the method and its effect on specific schemes are not described.

Marc HoubenLam L. Pham

other· Added 19 Oct 2026, published 18 Oct 2026

ethereum-magicians.orgpaper🛡️ Security

Post-Quantum Stateless Steganography & MTU Bounds: Bridging P2P Censorship Resistance to Quantum Networks

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.

eBPFXDPlibp2p+1

ML-KEMML-DSAHarvest-Now-Decrypt-Later+8

other· Published and added 17 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Eﬃcient Fixed-Point Implementation of Mitaka

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.

MitakaFalconC+1

MitakaZFN-DSAGaussian sampling+1

other· Published and added 17 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Power Analysis on SQIsign via Ideal Reconstruction from Quaternion Leakages

ePrint paper identifying five side-channel vulnerabilities in the signing procedure of Round-3 SQIsign, the isogeny-based signature scheme in NIST's additional post-quantum signature process. It gives polynomial-time algebraic algorithms that recover the secret key from leaked quaternion/ideal intermediate values, including cases where only partial bits leak. It also demonstrates power analysis on the SQIsign source code to extract the needed leakage, which earlier work on Round-1 or toy parameters did not do.

SQIsign

NISTideal reconstruction

Hardware & RF· Added 17 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

MLExpert: Towards a Generalist Agentic Model for Autonomous Machine Learning Engineering

Presents MLExpert, a 35B Mixture-of-Experts agentic model trained for autonomous long-horizon machine learning engineering. Training uses a Kaggle-MLE Factory to build executable tasks and an interaction harness, hierarchical agentic SFT with outcome-guided supervision, and Windowed Interaction RL (WIRL). WIRL treats the multi-turn decisions between successive experiment results as one execution window with a shared outcome reward, and uses asynchronous collection and policy correction. On MLE-Bench Lite the average medal rate rises from 37.9% to 72.7%, comparable to larger frontier models such as Kimi-K3 and GLM-5.3. It also improves on NatureBench Lite, agent scaffold optimization and algorithm optimization.

Mixture of ExpertsLLM

MLExpertKaggle-MLE FactoryMLE-Bench+4

research, training, agents· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Efficient KV Cache Migration for Geo-Distributed LLM Inference in Collaborative Edge Computing

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.

LLM

EdgeFlowarXiv

inference, research· Added 17 Oct 2026, published 15 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Hybrid BP-SAT Attack on MILENAGE: Combining the Complementary Strengths of Algebraic and Soft-Analytical Side-Channel Attacks

ePrint paper evaluating ASCA (SAT-based) and SASCA (belief propagation) side-channel attacks on the AES-based MILENAGE authentication algorithm in USIM cards, using neural-network leakage predictions to recover subscriber key K and operator secret OPc from a single trace. SASCA alone succeeds in 87.7% of attacks but converges to a wrong OPc in 6.1%; ASCA succeeds in only 34% within a 1-hour timeout. The hybrid, where BP refines the predictions before they go to the SAT solver, reaches 93.8% with lower average runtime. It also proposes a countermeasure that masks each AES round key with an independent per-device secret, and the authors report it defeated the hybrid attack in all evaluated cases.

AESMILENAGEUSIM

OPcSASCAASCA

Hardware & RF· Added 15 Oct 2026, published 14 Oct 2026

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