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arxiv.orgpaper🧠 AI

VeriCUA: Verification as Agentic Exploration for Computer-Using Agents

VeriCUA is a platform-agnostic framework that verifies task completion by computer-using agents through agentic exploration. The verifier inspects context to build a task rubric, then picks GUI or code checks for unresolved requirements and combines findings under explicit evidence rules. It works online and offline across desktop, mobile and web, and agrees better with reference labels than the baselines, with the largest offline gains on hard cases.

vision-language modelVeriCUA
arXiv

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

arxiv.orgpaper🧠 AI

Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution

Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.

Mixture of ExpertsC-MOPDon-policy distillation+1
Turing-20B-A2BarXiv

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

eprint.iacr.orgpaper🛡️ Security

Smoothness of Shortest Isogenies to Frobenius Conjugatess

Paper by Yi Ouyang, Wei Wei and Zheng Xu on the minimum degree of an isogeny from a supersingular elliptic curve E to its Frobenius conjugate E^(p). It bounds the probability that this degree is smooth, which makes Wesolowski's p^(1/3+o(1)) attack on the supersingular endomorphism ring problem unconditional. It also gives a uniform asymptotic formula, with Dirichlet L-values in the main term, for the probability that a connection isogeny of degree n exists.

supersingular endomorphism ring problemWesolowskiYi Ouyang+2

other· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

TrussTutor-Bench: Benchmarking large language models on solution accuracy, sycophancy, and error detection in statics

TrussTutor-Bench is a benchmark of 200 verified two-dimensional truss problems from statics, paired with automatically generated student solutions. It evaluates thirteen LLMs on solution accuracy (64.5% to 97.0%), error detection (90.4% to 100%) and sycophancy. Models rejected their own correct solutions in 1.6% to 50.0% of cases when a student insisted the work was wrong. Sycophancy separated models; error detection did not.

LLM
TrussTutor-BencharXiv

evals, research· Added 31 Oct 2026, published 28 Oct 2026

huggingface.copaper🧠 AI

What Effort Level Should I Choose? On the Economics of Reasoning Effort in LLM Agents

This paper treats LLM reasoning effort as a credence good and measures the counterfactual effect of effort levels on proprietary and open-weight models across four benchmarks, including long-horizon agentic tasks. Higher effort often fails on tasks lower effort solved, and on easy tasks the top level costs up to 42x the lowest. Starting at the lowest effort and escalating only after a verified failure matches or beats fixed top effort, usually at lower cost for proprietary models.

LLM
Hugging Face

research, evals, agents, ai-policy· Added 28 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Falcon Key Recovery from Joint Sampler Leakages

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.

FalconFN-DSAARM Cortex-M4+1
SamplerZc-fn-dsasoftplus interval regression+1

Hardware & RF· Added 28 Oct 2026, published 27 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

unicode.orgpaper🛡️ Security

UTS #39: Unicode Security Mechanisms

UTS #39 (Unicode Security Mechanisms) is the Unicode Consortium's technical standard for detecting identifier spoofing. It defines the General, IDN and Email security profiles for identifiers, confusable detection through skeleton and bidiSkeleton mappings, and mixed-script detection with resolved script sets and Restriction Levels. It also covers limited contexts for ZWJ/ZWNJ and the confusables and identifier data files. The fetched text is truncated, so later sections are not covered.

Unicode
homograph attackconfusableswhole-script confusables+7

AppSec, Learning· Added 26 Oct 2026, published 25 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Chosen Commitments and Certified Decoding Regions in the Adaptive Threshold Schnorr Attack

This IACR ePrint 2026/2632 paper by Demirtaş and Yayla analyses the Crites–Stewart adaptive forgery attack on threshold Schnorr signatures, which needs one call to a sparse linear search oracle (bounded distance decoding of a scaled Reed–Solomon code). It shows that choosing the commitment coefficient vector does not help much: a wrapper adversary with Q challenges succeeds with probability at most QM/p, even if its solvers decode every witness for free. For the six original (N, τ) pairs, M stays below 2^24, so the bound is under 2^-100 after 2^128 challenges at p = 2^252.

Schnorr signaturesReed–Solomon codesfolded Reed–Solomon codes
Crites and Stewart attackGuruswami–WangAshvinkumar–Habib–Srivastava+2

other· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

TriageRAG: Failure-Aware Self-Training for Adapting Retrieval-Augmented Language Models to Specialized Domains

TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.

RAGLLMQwen3
TriageRAGSimRAGCS-Bench+1

research, llm-apps, training· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

An actively trained neural operator for near-explicit atmospheric chemistry

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.

Fourier neural operatorMixture of Experts
Master Chemical MechanismFNO-MoEarXiv

research· Added 27 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ Security

VeilDNS: Post-Quantum DNS Privacy from the First UDP Packet

VeilDNS is a DNS-native protocol that uses a post-quantum KEM and AEAD to encrypt DNS questions and answers and authenticate the resolver in one UDP exchange on port 53, preserving the DNS wire format. It has stateless and stateful modes plus optional forward secrecy from the second query. Against ML-DSA-authenticated DoT/DoH/DoQ it reports up to 3.5x higher cold-session throughput and 13.7x fewer bytes; Tamarin proves query confidentiality, response authentication and forward secrecy.

DNSML-DSATLS+1
VeilDNSTamarinDoT+3

other· Added 26 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

PHYRD: A Living Benchmark for Reproducing and Extending Theoretical Physics Research

PHYRD is a living benchmark for theoretical-physics reasoning with 63 Reproduce problems (derive published results) and 43 Discover problems (consequences of modified theoretical conditions). Tasks are built by FormulaForest, with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy is 49.21% to 68.25% on Reproduce and 18.60% to 74.42% on closed-book Discover. FormulaForest data also lifts a Qwen-35B-A3B model on FrontierScience from 61% to 72%.

LLMQwen
PHYRDFormulaForestFrontierScience+2

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

eprint.iacr.orgpaper🛡️ Security

Balsamic: One-round Threshold UOV from GPU-Accelerated FHE

Balsamic is a complete homomorphic Unbalanced Oil and Vinegar (UOV) signer, used to build one-round threshold UOV signatures from a packed discrete-CKKS FHE circuit. A paired-pivot linear solver and pivot-success certificates allow encrypted retry selection. In 2-out-of-3 semihonest signing, each online partial signature takes 10.7 s on an RTX 6000 Ada and 7.77 s on a GB300, with 5.25 MB per response.

UOVCKKSFHE+2
Balsamicuniversal thresholdizerspaired-pivot solver+1

other· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Causal Chain of Thought: Mitigating Attention Leakage via Context Isolation

CausalCoT is an inference framework that splits a problem into a directed acyclic graph of atomic sub-questions and evaluates each node in an isolated context holding only its direct causal parents. This removes attention paths to non-parent content, so intermediate steps drive the answer rather than leaking from global context. It keeps benchmark accuracy competitive and raises causal sensitivity of reasoning steps by 12 points on average, up to 21, over the strongest baseline. The graph also allows test-time corrections that propagate downstream.

LLMTransformer
arXiv

research, prompting· Added 24 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Rethinking Sequence Compression with Test-time Regression

TTR-Compressor is a sequence compression method that treats input tokens as a test-time regression problem. The regression is learned end-to-end by backpropagation, and its coefficients are solved in closed form in the forward pass to give the compressed representation. As a block-wise sparse attention compressor it improves RULER over attention-based compressors by a large margin. As a visual token merger for multimodal LLMs it beats the MLP merger and matches it with 55.6% fewer visual tokens.

TransformerLLMvision-language model
TTR-CompressorRULERarXiv

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

arxiv.orgpaper🧠 AI

TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models

TRIAD is a benchmark for omni-modal ambiguity resolution. It has 327 hand-written text-image-audio queries in 86 scene groups across 18 ambiguity categories, and removing any one modality makes the answer non-unique. Across 14 omni-modal systems, the best model trails humans by over 20 points in group accuracy. Without answer options, model group accuracy falls to single digits, and leave-one-out tests show audio cues are often underused.

multimodal large language modelomni-modal LLM
TRIADarXiv

evals, multimodal, research· Added 23 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition

Sage-A1.5 is a 35B Mixture-of-Experts agentic model that builds on Sage-A1 for long-horizon problem solving. It adds three capabilities: orchestration with parallel subagents in isolated contexts, verification through progress checking, evidence validation and strategy revision, and open scientific discovery with inspectable research artifacts. The report shows them on complex agentic tasks and multidisciplinary science applications. The page gives only the abstract, no benchmark numbers.

Mixture of ExpertsLLM
Sage-A1.5Sage-A1arXiv

research, agents, models· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Reward Misspecification Degrades Chain-of-Thought Monitorability

This paper shows that reward misspecification alone degrades chain-of-thought (CoT) monitorability in language models. Using a testbed of paired true and misspecified rewards across three models and three tasks, CoTs keep reasoning about task intent while outputs hack the reward. The proposed reward description training injects an LM-inferred reward description into the input and raises monitorability by an average of 24% or 42% absolute, depending on the metric.

LLM
arXiv

ai-safety, training, research· Added 22 Oct 2026, published 20 Oct 2026

eprint.iacr.orgpaper🛡️ Security

safeRNSgcd: Accelerating Falcon key generation with RNS in SafeGCD

ePrint paper presenting safeRNSgcd, which computes the single Bézout coefficient needed at the deepest level of Falcon's NTRU solver while keeping large integers in a residue number system. Centered explicit CRT recovers the exact low bits needed by SafeGCD batches using single-word fixed-point arithmetic. A C implementation integrated into ntrugen reduces inversion and private-key basis-generation cost on a Raspberry Pi 5 and two x86-64 CPUs, versus the original ntrugen and a variant that skips the unused coefficient.

FalconntrugenSafeGCD+2
safeRNSgcdCesare HuangBo-Yin Yang

other· Added 17 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

MUB: An Identity-Masked Benchmark for Music Understanding in Multimodal Language Models

MUB (Music Understanding Benchmark) is a set of 500 identity-masked recordings with existing annotations for tempo, key, time signature and structural intervals. It covers all 24 keys, tempos of 47 to 180 BPM and 4,676 intervals, and strips titles, artists and platform IDs from inputs. Baselines reach 53.6% tempo accuracy (DSP) and 98.4% time-signature accuracy (constant 4/4). No multimodal LLM has been tested yet, and rights clearance is unresolved.

multimodal language model
Music Understanding BenchmarkMUBarXiv

evals, multimodal, research· Added 20 Oct 2026, published 16 Oct 2026

huggingface.copaper🧠 AI

Doc2Harness: Benchmarking LLMs on Long-Horizon Harness Engineering of Coding Agents

Doc2Harness is a two-stage benchmark for harness engineering. In Stage 1 a build model, working inside a fixed builder harness, gets an interface-only contract skeleton plus a development document consolidating behavior from twelve frontier harnesses, and builds a full candidate harness in an isolated container. In Stage 2 the frozen harness runs in separate per-test-case containers, driven by a scripted mock model, and is scored on four channels: requests sent to the model, files on disk, exit code and command output. Across 19 frontier models with three builds each on 328 test cases, GPT-6 Astra leads at 53.05% against 27.44% for the weakest, and failures cluster around how cross-cutting functionality is split across modules.

Docker
Doc2HarnessGPT-6 AstraHugging Face

evals, coding-agents, agents· Added 16 Oct 2026

arxiv.orgpaper🛡️ Security

Can SOLARBURST go silicon? A systematic analysis on software supply chain attacks for VLSI design flows

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.

SolarWindsSUNBURST

Supply Chain, Hardware & RF· Added 20 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Value as Memory: Online Value-Guided Post-training for General Robot Policies

Value as Memory (VaM) is a post-training framework for generalist robot policies. It addresses partial observability and the delayed feedback of offline post-training. Value caching conditions actions on recent value predictions as task-progress memory. Online real-world RL combines offline demonstrations, off-policy interventions and on-policy rollouts, and moves from offline RL warmup to autonomous learning with sparse rewards. On long-horizon deformable-object and contact-rich manipulation tasks it gains 29.4% mean absolute success rate from value caching and 32.8% from online learning over matched offline training. The online schedule runs 8x as many policy iterations under the same gradient-step budget, with over 2x lower time per gradient step from concurrent data collection and training.

Value as MemoryarXiv

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

arxiv.orgpaper🧠 AI

AI Consilium: a Benchmark of LLM Clinical Multidisciplinary Second Opinions

AI Consilium is a multidimensional benchmark built from 300 diagnostically resolved NEJM–MedQA cases (110 for physician reference ratings and Judge calibration, 190 held out). Eight single-model pipelines produced role-specific opinions and interactive-consilium reports under free-form and structured clinical-reasoning conditions, and a physician-calibrated LLM Judge scored 3,040 final reports. Model identity separated quality more than format; structured reports won in seven of eight pipelines (management 2.740 vs. 2.436), multidisciplinary synthesis beat the primary-role opinion on all five shared criteria, and GPT-5.6-Sol led every component with the fewest review rounds.

LLM
AI ConsiliumNEJMMedQA+2

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

arxiv.orgpaper🧠 AI

EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search

EliteReplay is an RL post-training recipe that pairs GRPO with an "elite" archive built by quality-diversity search. Before training, the frozen base model is used to collect solutions spanning distinct approaches; during training, a replay loss on this archive is added to GRPO. This counters the narrowing of solution diversity that RL causes. On text-to-SQL tasks it reaches higher pass@128 than all baselines and yields up to 77% more distinct correct strategies. It also retains more of the base model's strategies and covers both readings of ambiguous questions nearly twice as often on AmbiQT.

GRPOLLM
EliteReplayAmbiQTarXiv

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

arxiv.orgpaper🧠 AI

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

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.

CNNGANTopology ToolKit
arXiv

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

eprint.iacr.orgpaper🛡️ Security

Threshold Post-Quantum E-Voting from Lattice Commitments

T-EVOLVE extends the lattice-commitment e-voting protocol EVOLVE to threshold tallying. Votes are Shamir-shared, so any t of n authorities can finish the tally. Privacy holds against t-1 colluding authorities and verifiability holds even if all of them collude. The voter proves her committed shares form a Reed-Solomon codeword whose value at zero is a valid vote. Complaints about ciphertexts that do not decrypt are publicly verifiable, using an LWE-instantiable encryption scheme with publicly verifiable opening. Privacy, verifiability, individual verifiability and robustness are proven in the classical random oracle model, and the verifiability reduction is not tight. In a C implementation, a yes/no ballot for four authorities takes 109 KB, and an authority aggregates 10^4 ballots in about three minutes on one core.

C
T-EVOLVEEVOLVEShamir secret sharing+2

other· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Recursive Reward Improvement via Adversarial Meta-Prompt Evolution

Introduces Recursive Reward Improvement (RRI) for recursive self-improvement on open-ended expert tasks without answer keys, where a proposer writes both a task and a rubric. Before the solver trains, an adversary writes a rubric-gaming but useless answer, the same model writes an honest answer without seeing the rubric, and up to two repair steps revise the rubric until the honest answer wins. Exploits found also rewrite future task and rubric generation. On HealthBench Professional it beats a Generation Only baseline by 10.9 points at 9B and 12.4 at 27B (10.3 with no external model), and the 27B model transfers to PRBench Hard (+6.6) and ProfBench (+8.0).

LLM
HealthBench ProfessionalPRBench HardProfBench+2

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

arxiv.orgpaper🧠 AI

Thinking in Graphs: Neo4j-Backed Exploit-Chain Reasoning for LLM Penetration-Testing Agents

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.

Neo4jModel Context ProtocolClaude Code+4
ChainGraphGOADarXiv

agents, mcp, research· Added 17 Oct 2026, published 14 Oct 2026

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