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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

arxiv.orgpaper🛡️ Security

When LLM Architectures Meet Smart Contract Vulnerability Detection: a Replication Study

This arXiv paper (October 2026) replicates Chen et al.'s study of LLM-based smart contract vulnerability detection. It confirms the high-recall, low-precision pattern on GPT-4, then tests Mixture-of-Experts and RNN-like models plus a preliminary State Space Model run. Gemma-3 matches GPT-4's profile despite far fewer parameters, while two Mixture-of-Experts models diverge, so architecture family alone does not predict detection behavior.

GPT-4GemmaLLM
Chen et al.

AI Security, AppSec· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

From Leniency to Suspicion: How an Open Post-Training Recipe Changes Whom LLM Fact-Checkers Believe

This paper measures job-based doubt in LLM fact-checkers: judging a claim false more often when the poster has a lower-status job. It compares base and post-trained checkpoints of six model families on fact-checked claims with randomised poster profiles. Only the open Tülu-3 recipe (OLMo-2 32B, Tülu-3 70B) creates the effect, under one percentage point but reliable. In OLMo-2 32B the flip comes from one residual-stream direction and appears at supervised fine-tuning, confirmed on 400 new claims. None of seven commercial models doubts lower-status posters more.

LLMTransformer
OLMo-2 32BTülu-3Tülu-3 70B+3

research, ai-safety, training· Added 30 Oct 2026, published 28 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Poly2MsgLeaks: Certified Key Recovery from Polynomial-to-Message Leakage

Poly2MsgLeaks is a profiled chosen-ciphertext power-analysis attack on the SMAUG-T (TiMER parameter set) post-quantum KEM that recovers the full 512-coefficient PKE secret from six traces on an STM32F415. It recovers 999 of 1,000 unseen secrets and proves an incorrect key is released with probability at most 2^-2095.6. Single-write message assembly and a two-share masked P2M resisted the evaluated six-execution attack.

SMAUG-TSTM32F415
Poly2MsgLeakschosen-ciphertext attackpolynomial-to-message leakage+1

Hardware & RF· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Non-profiled Single-Trace ML-DSA Secret Key Recovery by Horizontal Distribution Analysis

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.

ML-DSAARM Cortex-M4
Hamming weight leakagehorizontal attacknon-profiled attack+4

Hardware & RF· 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

arxiv.orgpaper🧠 AI

Detecting and Understanding Ambiguity in Visualizations with Role-Playing Vision Language Model Interpretations

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

vision-language modelLLM
arXiv

research, multimodal, evals· Added 27 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🛡️ Security

Ranking Is Not Deciding: Benchmark Shortcuts and Operating-Point Collapse in Query-Time RAG Poisoning Defenses

Ranking Is Not Deciding is an October 2026 arXiv paper showing that query-time RAG poisoning filters score near-perfectly because of how the poison set was built. PoisonedRAG's code prepends the target question to each passage. Removing that prefix takes RAGuard from AUROC 0.953 to 0.758 and its miss rate from 0.191 to 0.763. The authors release a script that profiles a poison set.

LLM
PoisonedRAGRAGuardTrustRAG+5

AI Security· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases

This paper studies demographic bias in LLM-emulated electorates, using three U.S. presidential elections and comparing one closed-source and one open-source LLM. Removing gender from the voter profile cuts the largest group error by 22-42%, and removing education cuts it by 64%. Removing self-reported race raises mean group error by over 40%. Repeated queries and fairness instructions do not reduce errors and can double them.

LLM
arXiv

research, ai-safety, evals· Added 27 Oct 2026, published 23 Oct 2026

arxiv.orgpaper🧠 AI

Profile-Driven User Simulation via Reinforcement Learning with Hierarchical Rewards for Task-Oriented Dialogues

ProfileSim is a profile-driven user simulator for financial marketing outbound calls. It models user profiles (demographics, Big Five traits) and business profiles (intents reverse-engineered from real call logs). The base model is fine-tuned on real voice dialogues, then refined with GRPO using dialogue-level and utterance-level rewards. Against other user generators it scores highest on colloquial naturalness and profile adherence, and lowest agent score on long-tail intents, with shorter utterances at comparable business compliance.

GRPORAGLLM
ProfileSimarXiv

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

arxiv.orgpaper🧠 AI

IFC-MCQ: Automatically Generating Multiple-Choice Benchmarks for Evaluating LLM Agents on IFC Data Retrieval

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.

LLMIndustry Foundation Classes
IFC-MCQarXiv

evals, agents, research· Added 23 Oct 2026, published 21 Oct 2026

eprint.iacr.orgpaper🛡️ Security

SHAKEing SLH-DSA with Non-profiled SCAs

SHAKEing SLH-DSA with Non-profiled SCAs is an eShard ePrint paper (2026/2584) on correlation power analysis of SLH-DSA-SHAKE, the NIST-standardised hash-based signature scheme. It analyses PRF inputs and selection functions, then combines subtrace averaging, partial-then-total CPA and an efficient exhaustive search to recover the full secret seed, enabling universal forgery. On a 32-bit microcontroller the attack needs about 280 to 559 signatures, and the search finishes within 45 minutes on 72 cores.

SLH-DSASHAKEKeccak
NISTuniversal forgerySPHINCS+

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

arxiv.orgpaper🛡️ Security

Where AI Vulnerability Discovery Harnesses Go Quiet: Structural False Negatives and Hidden Vulnerabilities

Paper (arXiv, 21 October 2026) on structural false negatives in LLM vulnerability discovery harnesses. Starting from Knostic's OpenAnt and raptor, then 10 more open-source harnesses, the authors map 13 points where code selection, filtering, and finding storage or merging drop vulnerabilities. One file was dropped by six harnesses before the model saw it, five without notice. It covers attacker evasion, content-steered verdicts, prompt injection, and design principles and tests.

OpenAntraptorLLM
KnosticPtacek and Newshamprompt injection+1

AI Security, AppSec· Added 24 Oct 2026, published 21 Oct 2026

arxiv.orgpaper🛡️ Security

After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents

CarryoverBench is a benchmark of 12 personal-assistant scenarios and 101 attack tasks for carryover attacks, where a one-time indirect prompt injection planted in agent memory files or scheduled tasks re-fires in later sessions. Three trigger channels (Instant, Autonomous, Latent) are scored by a rule-based verifier with separate planting and triggering stages. Across five frontier models, payloads re-fire over five rounds without measurable decay. Porting the Latent channel to the Hermes runtime weakens the effect but does not remove it.

LLM
CarryoverBenchcarryover attacksindirect prompt injection+1

AI Security· Added 22 Oct 2026, published 20 Oct 2026

arxiv.orgpaper🧠 AI

Same Simulation, Different Evidence: Where Retrieved Tool Output Costs LLM Agents Accuracy

This paper studies how keeping long tool output out of the context window, and reading it through a section index instead, costs LLM agents accuracy. Using the PHREEQC geochemistry simulator on 158 execution-verified multiple-choice items and 11,007 logged calls, GPT-5.1 and GPT-5.2 lose 11.3 and 7.9 points, while Claude Opus 4.6 and Sonnet 4.6 show no detectable cost. The authors name the failure evidence substitution: agents skip the report and misround from their own output files.

PHREEQCLLMClaude+1
USGSGPT-5.1GPT-5.2+4

research, agents, evals· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Blind-DLPA: An End-to-End Framework for Profiled Blind Side-Channel Analysis

Blind-DLPA is a multi-task deep-learning framework for profiled blind side-channel analysis, where the attacker has labeled profiling traces but only leakage traces from the target, with no plaintexts or ciphertexts. It estimates Hamming-weight posteriors of an input and a key-dependent intermediate, then ranks key hypotheses against theoretical joint distributions. It recovers keys on unprotected AES and Kyber and on ASCAD (guessing entropy zero with 39 traces), and the authors report collisions in a prior Ascon Hamming-weight model.

AESKyberAscon
Blind-DLPAASCADprofiled blind side-channel analysis+4

Hardware & RF· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Sparse Secrets, Dense Problems: Efficiently Masking HQC in Hardware

ePrint paper presenting the first fully masked hardware implementation of HQC (key generation, encapsulation, decapsulation) on FPGA. It introduces a mixed Boolean/arithmetic masking for sparse-dense vector multiplication, proven first-order secure in the transition- and glitch-robust probing model and PINI-composable, at only 3% extra cycles and 81 random bits per cycle. It also shows a profiled horizontal side-channel attack on Rotate-and-Add multipliers that recovers support indices within ±8 positions from 200 traces despite TVLA showing no leakage after 10 million traces, and adds integrated hiding countermeasures costing 1.7% latency.

HQCML-KEMFPGA+1
PINITVLAhorizontal side-channel attack+2

Hardware & RF· Added 17 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

My Credentials, Please: Detecting Filesystem Capability Laundering in MCP Servers at Scale

Studies "filesystem capability laundering" in MCP servers: client approval of a tool call grants the server process broad host filesystem access, so attacker-steered arguments can reach sensitive files outside the tool's declared contract. The authors build MCP-FSD, which infers per-tool least-privilege boundaries, plants credential fixtures outside them, and tests exploitability and sandbox containment by paired dynamic execution. Of 21,097 servers from nine registries, 16.4% of 4,806 triaged servers have filesystem capabilities despite non-filesystem classifications. Dynamic validation confirmed 150 vulnerable instances, and disclosure produced 13 advisories, including two CVEs in Anthropic's Git MCP Server.

Model Context ProtocolAnthropicGit
filesystem capability launderingMCP-FSDpath traversal+1

AI Security, AppSec· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

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.

LLMClaude CodeRAG
Librarian AgentsBrowseComp-PlusBRIGHT+1

research, agents, llm-apps· Added 17 Oct 2026, published 15 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Accelerating Pollard Rho with GPU Field Arithmetic on NVIDIA Blackwell

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.

CUDANVIDIA BlackwellAWS+1
Pollard rhoECC2K-130Certicom challenge+5

other· Published and added 14 Oct 2026

arxiv.orgpaper🧠 AI

MultiAlloc: Budget-Aware Precision Allocation for KV Cache Quantization

MultiAlloc is a profiling-guided framework for KV cache quantization. It splits a fixed KV memory budget between layer-wise precision enhancement and token-wise preservation. Lightweight offline profiling measures layer sensitivity and token importance, and the profiled memory–accuracy trade-off yields an allocation policy with no online search. The paper reports gains over layer-wise, token-wise and recent multi-granularity KV quantization baselines at matched memory on reasoning and long-context benchmarks, across Mistral, Llama, DeepSeek-Qwen and QwQ models.

LLMTransformer
MultiAllocMistralLlama+4

inference, research· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🛡️ Security

Kairos: Transactional Security Enforcement at the System Boundary for LLM Agents

Kairos is a reference monitor placed at the isolation boundary of an unmodified, opaque LLM agent, where tool calls, shell commands and agent-written scripts all appear as the same file and network operations. A declarative trust policy decides most effects deterministically and defers the rest; deferred effects are held while the agent continues speculatively, and a bounded-authority decision oracle (an LLM or a human) releases or refuses them based on the trace. Across three benchmarks and three agent models it beats existing defenses on the attack-success/utility trade-off; on AgentDojo it cuts attack success from 47.0% to 2.7% with no loss of benign utility.

LLM
indirect prompt injectionAgentDojo

AI Security· Added 15 Oct 2026, published 13 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Breaking First-Order Masked Kyber with Belief Propagation Alone

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.

KyberARM Cortex-M4
ML-KEMOnsager correctionHamming weight leakage+1

Hardware & RF· Added 14 Oct 2026, published 12 Oct 2026

arxiv.orgpaper🛡️ Security

Physical Side-Channel Attacks on Edge LLMs: Extracting Output Tokens and User Prompts

Presents an EM side-channel attack on LLaMA-3.2-1B-Instruct running on a Raspberry Pi 5, showing passive physical measurements leak generated tokens and user prompts. Three attacks: a profiling attack (97.2% accuracy over 50 medical/privacy keywords), a hidden-state reconstruction attack predicting tokens from the 128k vocabulary (94.3% on profiled classes; 67% of unseen words ranked in top 100), and a hidden-state inversion attack reconstructing prompts semantically. Shows that local edge inference does not guarantee confidentiality of tokens and inputs.

LlamaLLMRaspberry Pi 5
electromagnetic side-channel attackhidden-state inversion

Hardware & RF, AI Security· Added 16 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🛡️ Security

Understanding Verification Vulnerabilities in Cross-chain Bridges: An Empirical Study

First systematic empirical study of Cross-chain Verification Vulnerabilities (CVVs) in bridge smart contracts, such as flawed validation of state proofs, message sender identities and signatures. From 588 audit reports across 36 bridge projects it derives 11 CVV patterns in 5 categories and builds a dataset of 830 instances across 786 contract files, mapped to reports, source versions and pattern labels. Existing tools cover few CVV patterns, and only two of four tested LLMs beat a 29.3% Macro-F1 constant baseline (best 51.2%), which motivates detectors built for cross-chain verification semantics.

LLM
Cross-chain Verification VulnerabilitiesCVV

Blockchain· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents

SmartSalesBench is a multi-turn conversational benchmark that tests LLM sales agents against psychologically grounded, resistant buyer personas over conversations of up to twenty turns. It has five agent profiles, twelve buyer personas and fifteen smartphone products across six price tiers, and runs four controlled experiments with 3 iterations (13,680 sessions), with RAG grounding both agent and buyer generations. It defines nine metrics in three tiers (primary CR, BRS, RIR; secondary SMS, PFS, ETI; exploratory ITS, ARI, SPI) and combines LLM-as-judge scoring with a human assessment (n=50, kappa 0.71, Pearson r 0.81 with BRS). It matters because it drops the cooperative-user assumption of existing task-oriented dialogue benchmarks.

RAG
SmartSalesBencharXiv

evals, agents· Added 12 Oct 2026, published 9 Oct 2026

arxiv.orgpaper🧠 AI

PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies

PathTime-VLA reparameterizes Vision-Language-Action actions as a progress-indexed interaction path X(s) plus a positive interval-time profile that defines a monotone time law t(s). Geometry is thus decoupled from execution speed, so teleoperation timing artifacts don't contaminate the path target. A staged post-training recipe uses demonstrations and DAgger interventions for a target-domain prior, Speed-DQN to learn execution-speed multipliers from robot interaction, and Path-AWR to refine the diffusion path generator from rollout outcomes. On three tasks it reaches 58/60 successes versus 57/60 for BC+DAgger at fixed 1×, with about 39–52% shorter mean completion times on successful trials.

Vision-Language-Actiondiffusion modelDAgger+2
PathTime-VLA

research, training, multimodal· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 AI

Skill Constellations: Tracing the Supply Chain of Agent Skills on GitHub

The paper builds the first dated copy network of agent skills (SKILL.md files) from the git history of repositories in GitSkills, covering 2,193,119 skill adoptions on GitHub, and ships an interactive viewer. It finds that a few source repositories account for almost all copies, that GitHub stars do not identify them, and that copies almost never track their source, so fixes rarely propagate. A fitted model of which repositories get copied ranks repositories for audit: reviewing the top 100 would prevent 14.9% of later adoptions of high-risk skills, against 0.5% for the 100 most-starred. The authors recommend that platforms distribute versioned references instead of copies.

Claude CodeCodexGitHub+1
GitSkills

coding-agents, prompting, research· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🛡️ Security

Skill Constellations: Tracing the Supply Chain of Agent Skills on GitHub

Builds the first dated copy network of agent skills (SKILL.md files) from git history in the GitSkills dataset, covering 2,193,119 skill adoptions on GitHub, plus an interactive viewer. Finds a few repositories are the source of nearly all copies, stars do not identify them, and copies almost never track their source, so upstream fixes rarely propagate. A model of which repositories get copied ranks repos for audit: reviewing the top 100 prevents 14.9% of later adoptions of high-risk skills versus 0.5% for the 100 most starred. The authors recommend platforms distribute versioned references rather than copies.

Claude CodeCodexGitHub+1
GitSkillsSKILL.md

Supply Chain, AI Security· Added 9 Oct 2026, published 8 Oct 2026

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