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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

Can LLMs Uncover Hidden Physical Laws through Free Exploration?

LongChang is an automated framework that builds simulated worlds with altered physical laws, such as heavier objects falling more slowly, to test whether LLMs can find the law by free exploration. Explorer LLMs manipulate objects and build tools without tasks or hints, while separate LLM agents generate scenes, review tool designs and grade reports. Across 12 LLMs, GPT-6 Astra reached a 16.7% discovery rate and the other 11 scored 0%.

LLM

LongChangGPT-6 AstraarXiv

evals, research, agents· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

AK_QMUL at ArGuard Shared Tasks: A Single Fine-Tuned JAIS for Arabic Harmful-Prompt Detection

AK_QMUL's system for the ArGuard-2026 shared tasks fine-tunes JAIS-6.7B with 4-bit QLoRA and a linear head on the last-token representation. For binary safe/unsafe detection (B1) it uses a four-way head over dialect register and AraSafe MSA augmentation, reaching macro-F1 0.984. For seven-way harm domain (B2) it averages five seeds, reaching 0.790. Both ranked first. Bully is the weakest class.

JAIS-6.7BQLoRALLM

JAISArGuardAraSafe+2

research, ai-safety, training· Added 28 Oct 2026, published 26 Oct 2026

arxiv.orgpaper🧠 AI

Task Format as a Factor in Adaptation: A Controlled Comparison of LoRA and Full Fine-Tuning for Prompt-Safety Classification

This paper compares LoRA and full fine-tuning for prompt-safety classification using a matched 400M encoder-decoder pair (Ettin) across three task formats, with up to 15 seeds per cell and pre-specified analyses. Full fine-tuning leads LoRA by 2-3 pp at the pre-specified learning rates, and the encoder gap closes when both use the same learning-rate search. Training LoRA to write a reason after its verdict adds 4.0 pp, while a content-free sentence of equal length does not. Trained models beat a prompted 8B in domain but trail it by 8-23 pp on adversarial jailbreaks and over-flag benign prompts (27-66% against 2-3%).

LoRAEttinTransformer

training, research, evals· Added 27 Oct 2026, published 24 Oct 2026

arxiv.orgpaper🧠 AI

When to Stop Penalizing Actions? Martingale GRPO for VLA Reinforcement Learning

MART-GRPO is a variant of group-relative policy optimization for vision-language-action (VLA) reinforcement learning. It uses a LoRA-fine-tuned VLM to find the last visible error without recovery in a failed rollout video, keeps failure feedback up to that point, and drops it afterward. Martingale stopping conditions show this preserves the expected GRPO gradient and can reduce covariance. With OpenVLA-OFT on RoboTwin 2.0, it raises success rates over full-trajectory GRPO across multiple tasks.

GRPOLoRAvision-language-action model+2

RoboTwin 2.0arXiv

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

arxiv.orgpaper🧠 AI

MAST-VLA: Skill-Conditioned Adaptation with Trajectory-Affordance Priors for Multi-Skill Robot Manipulation

MAST-VLA is a vision-language-action framework for multi-skill robot manipulation that conditions on both the skill to execute and where and how to contact objects. It adds skill-conditioned LoRA Mixture-of-Experts adapters to the LLM backbone and turns a predicted affordance heatmap into a patch-level spatial prior for the action head. It reaches an Avg. Len of 4.65 on CALVIN ABC→D and was tested on a real robot.

vision-language-action modelLoRAMixture of Experts+1

MAST-VLACALVINarXiv

research, multimodal· Added 26 Oct 2026, published 23 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

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

arxiv.orgpaper🧠 AI

Predictable Scaling: Efficient Model Exploration via Scaling-Law Prediction

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.

Gaussian Process

arXiv

research, training· Added 20 Oct 2026, published 16 Oct 2026

arxiv.orgpaper🧠 AI

Rethinking Routing Exploration in Modern Mixture-of-Experts

The paper identifies an "exploration dilemma" in modern Mixture-of-Experts models. Shared experts reduce the variability of token representations, which makes routing less exploratory. Fine-grained expert segmentation enlarges the combinatorial routing space and so needs more exploration, which means its extra expressiveness may go unrealized. It proposes MirrorMoE, which shrinks the exploration space by suppressing unnecessary experts per token, and reports consistent gains across model scales.

Mixture of Experts

MirrorMoEarXiv

research· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

PROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents

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.

LLM

PROBEGRPODPO+1

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

arxiv.orgpaper🧠 AI

PROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents

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.

GRPODPOGFlowNet+1

PROBEarXiv

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

arxiv.orgpaper🧠 AI

SP$^2$ec: Adaptive Self-Speculative Decoding for Vision-Language Models

SP$^2$ec (Single-Peak SPeculative decoding) is a training-free bandit algorithm that adapts the draft configuration of self-speculative decoding per prompt for vision-language models. It relies on the single-peak throughput pattern seen in SWIFT, KnapSpec, SpecVLM and the authors' cosSim method. It minimizes a new wall-time regret measure and has theoretical guarantees of near-optimal configuration with negligible exploration cost. On Qwen2.5-VL-7B-Instruct and LLaVA-NeXT-Mistral-7B it improves over the original self-speculative methods by up to 11% on visual and textual tasks.

vision-language modelQwenMistral

SP$^2$ecSWIFTKnapSpec+5

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

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

The paper tests whether LLM legal explanations reflect what drives the verdict. Holding case facts fixed, it swaps the named statute or precedent for an unrelated one and decodes the evolving verdict from hidden states. Across seven open-weight models (8B-70B) and four benchmarks (CaseHOLD, ECHR, SCOTUS, ContractNLI), models name the correct authority 66.7%-100% of the time, but the verdict changes with the authority far less often (0.0%-21.7% on CaseHOLD, 30.0%-76.7% on ECHR and SCOTUS, 43.3%-50.0% on ContractNLI). Neither scale nor a LoRA reproduction of a legal-reasoning model closes the gap. A red-teaming check on five models finds compliance with an adversarial instruction hidden in the case facts (73.3%-96.4%) far exceeds verdict-swap sensitivity. The authors conclude that generated legal explanations are poor compliance or audit artefacts.

LoRA

CaseHOLDECHRSCOTUS+1

research, evals, ai-safety· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🛡️ Security

An Exploration of Attacks on AI Agent Systems

Paper that tests prompt injection against AI agents through both direct user input and inter-agent communication. The attacks leak sensitive information and trigger cross-agent infections. Most findings concern single-agent systems, and the authors then extend the attacks to multi-agent frameworks to show how weaknesses propagate between agents. It argues that agents embedded in real-world workflows need stronger defenses.

LLM

prompt injection

AI Security· Added 12 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🛡️ Security

SLDR: Defending Against Malicious Fine-tuning via Selective Layers Recovery and Dynamic Routing

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.

LLMLlamaLoRA

AI Security· Added 9 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

SLDR: Defending Against Malicious Fine-tuning via Selective Layers Recovery and Dynamic Routing

SLDR is a post-fine-tuning defense that protects aligned LLMs from malicious fine-tuning-as-a-service attacks, which can quietly erode refusal behavior while keeping task performance intact. It trains a LoRA recovery adapter only on the layers with the most extreme (signed) safety sensitivity scores, then uses representation-based dynamic routing to activate that adapter solely for malicious queries at inference time. Evaluated across four model architectures, five downstream tasks, and four harmful-content benchmarks, it drives the average harmful score on Llama3.1/SST2 from 11.54 down to 0.08 while preserving downstream accuracy, and stays near zero even at poisoning ratios up to 0.9.

LoRALlama

Llama3.1SST2

ai-safety, training, research· Added 8 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

A Fine-Grained Analysis of the LoRA Fine-Tuning Landscape with Implications for Data Selection

This paper develops a sharper theoretical landscape analysis for Low-Rank Adaptation (LoRA) fine-tuning, introducing LoRA-RIP, a data-dependent restricted-isometry metric that measures how well-conditioned the cross-entropy objective is along LoRA-relevant low-rank directions. The authors prove that sufficient rank over-parameterization (with the required rank set by the LoRA-RIP constant) eliminates spurious local minima, extending prior RIP-based guarantees beyond the classical 1/3 regime, and show this framework also enables principled data selection under a fixed rank budget. Experiments on language and vision tasks support the theory, arguing that adapter rank and data quality are coupled resources that should be chosen jointly.

LoRA

LoRA-RIP

training· Added 6 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🧠 AI

HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning

HeuFouFT is a task-guided framework for selecting which frequency coordinates to train in Fourier fine-tuning (FourierFT), replacing fixed uniform or Gaussian band-pass coordinate selection with a search driven by downstream task performance. It initializes three metaheuristic optimizers (GA-SA, PSO, Cuckoo Search) from a lightweight block-level probe intensity map, using a Random Forest to filter candidate coordinate sets before proxy fine-tuning. On the E2E benchmark with GPT-2-Medium, all three variants beat random-uniform and Gaussian FourierFT and LoRA, with PSO beating the LoCA baseline on four of five metrics while using 37.6% fewer trainable spectral coefficients and only 15-18% of full fine-tuning FLOPs.

LoRAGPT-2

GPT-2-MediumE2ELoCA+1

training· Added 6 Oct 2026, published 5 Oct 2026

arxiv.orgpaper🧠 AI

Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes

Evaluates vanilla Double Deep Q-Learning (DDQL), K-means-partitioned mixtures of DDQL experts (K=2, 4, 8) and parameter-matched dense networks for order execution on 5-minute BTC/USDT limit order book data from Binance. No learned configuration significantly beats DDQL on implementation shortfall, and all are worse than TWAP and immediate liquidation in this frictionless replay environment. Ablations show annealed exploration alone removes the collapse to wait-until-forced-liquidation policies (12/100 to 0/100), so expert partitioning appears to mask a training-specification flaw rather than add real benefit. The attribution of the failure mode reverses between 30 and 100 seeds, which makes the paper a case for repeated-seed evaluation in RL.

Mixture of ExpertsDouble Deep Q-LearningK-means

arXivBinanceTWAP

research, training, evals· Added 15 Oct 2026, published 2 Oct 2026

arxiv.orgpaper🧠 AI

Harnessing Domain Specialists in Multimodal Mixture-of-Experts for Efficient Adaptation

This arXiv paper studies emergent semantic specialization among experts in multimodal Mixture-of-Experts (MoE) models and introduces ExpertLens, a data-free method that identifies domain-specialized experts by decoding router weights into vocabulary tokens from pretrained weights alone. Using this to selectively fine-tune only the relevant experts, the authors match or beat full fine-tuning on math, medical, and remote-sensing tasks while updating only 21.7-47.0% of parameters, achieving a 4x training speedup and outperforming LoRA in both performance and efficiency.

Mixture of ExpertsLoRA

ExpertLens

research, training, multimodal· Added 2 Oct 2026, published 1 Oct 2026

arxiv.orgpaper🧠 AI

Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling

This paper introduces Parallel Power Tempering (PPT), an inference-time sampling method that runs multiple interacting replicas of an LLM at different power-sharpening levels (parallel tempering) to balance exploration of diverse reasoning paths against exploitation of high-probability correct trajectories. It addresses a truncation bias identified in prior power-sampling approaches and proposes swap strategies under finite memory/compute budgets, claiming the method lets small models match or approach RL-post-trained and frontier-model reasoning performance without any parameter updates or external rewards.

research, inference· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

EvE: An Alternate Optimizer to Adam

EvE (Evolutionary Explorer) is a steady-state, population-of-four differential evolution optimizer with a targeted Adam fallback, proposed as an alternative to Adam for cheap, early ranking of configurations in hyperparameter and architecture search. Under a fixed evaluation-cost-matched budget it wins or ties Adam on 76% of 70 (problem, dimension) cells across seven scalable benchmarks up to one million variables, and on real tasks (MLP on MNIST, LoRA fine-tuning of a 1.5B-parameter LLM on two datasets) it finishes 1.7-3.9x faster at a modest quality cost. Inside successive halving on UCI Adult it completes hyperparameter/architecture searches 3.1-3.5x faster while ranking configurations nearly as consistently as Adam does with itself across seeds (Kendall's tau 0.66-0.69).

LoRA

MNISTGSM8KUCI Adult

training, research· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

VulContextBench: A Benchmark for Security Context Retrieval in Coding Agents

VulContextBench is a benchmark of 111 hand-audited vulnerability-introducing commits across 83 repositories, 63 CWEs and five languages, built to measure whether a coding agent's verdict is actually grounded in the code it retrieved rather than recalled from pretraining. Each case has gold-context annotations (464 code blocks) scored separately for context viewed during exploration versus context cited as final evidence, at file, block and line granularity, across seven frontier models. The headline finding is a large viewed-vs-reported gap — e.g. Qwen3-Coder-Next views 86.3% of gold lines but cites only 12.9%, GPT-5.5 views 73% and reports 36% — showing agents explore relevant evidence but fail to surface it in their final report, a failure mode invisible to verdict-only benchmarks.

Qwen3-Coder-NextGPT-5.5

VulContextBench

evals, coding-agents· Added 2 Oct 2026, published 26 Sep 2026

arxiv.orgpaper🧠 AI

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

CWE-Trace is a benchmark and diagnostic framework of 834 manually curated Linux kernel samples spanning 74 CWEs, built with a strict temporal train/test split to rule out data contamination, used to evaluate eight vanilla LLMs and 15 LoRA fine-tuned variants on vulnerability detection and CWE classification. The paper introduces two new metrics — Directional Failure Index (DFI) and Hierarchical Distance and Direction (HDD) — and finds that fine-tuning only recalibrates output thresholds rather than improving actual security reasoning, with the best model reaching just 52.1% detection accuracy (barely above chance) and under 1.3% Top-1 CWE classification accuracy.

LoRA

CWE-TraceDeepSeek-R1Directional Failure Index+2

evals, research· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Agent-Editing World Model: Rethinking World Modeling for LLM Agents

This arXiv paper proposes the Agent-Editing World Model (AEWM), which reframes world modeling for LLM agents: instead of predicting/reconstructing environment observations (high-entropy tool responses), it models how reasoning and actions shape future task progress, addressing "task-state contamination" where stale plans and unsupported assumptions corrupt later decisions. It introduces Action Judge (classifying decisions as Critical, Exploratory, or Noisy) and State Revision, combined via EditAct which directly edits the agent's state using real execution feedback rather than issuing critiques. Trained across Search, Terminal, and Software Engineering domains, AEWM reports 70.5% macro-F1 on its Action Judge benchmark (+10.6 over the strongest frontier baseline) and EditAct improves average scores by 3.2–6.7 points across six benchmarks and three agent backbones, with an RFT variant (AEWM-RFT) also outperforming Self-RFT.

AEWMAction JudgeState Revision+3

agents, research· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

Incident-Data Robustness Analysis of the OWASP Top 10 for LLM Applications (2026): How a Community-Expert Ranking Holds Up Against a Large-Scale LLM Incident Corpus

This arXiv paper stress-tests the OWASP Top 10 for LLM Applications (2026) community-expert ranking against a large-scale corpus of real LLM-security incidents (7,714 snapshotted, 6,639 labeled) drawn from CVE, GHSA, OSV, and AIAAIC. Using a Bayesian measurement-error model to correct incident counts for classifier precision/recall, the authors find weak agreement between the expert ranking and the incident-based ranking (Cohen's kappa about 0.20), yet the expert list proves robust: a bake-off of four frontier LLM classifiers fails to beat a simple incidence-floor baseline (balanced accuracy 0.863, Spearman rho = 0.918 against held-out ground truth). It is framed as an exploratory analysis by two OWASP working-group members, not an official supersession of the list.

OSV

OWASP Top 10 for LLM ApplicationsAIAAICGHSA

AI Security· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software

This paper introduces CWE-Trace, a benchmark of 834 manually curated Linux kernel vulnerability samples spanning 74 CWEs with a strict temporal split to eliminate training data leakage. Evaluating eight vanilla LLMs including DeepSeek-R1 and fifteen LoRA fine tuned variants, the authors find contamination gives no real advantage and that fine tuning only shifts output calibration rather than underlying security reasoning, with the best model barely beating chance and CWE ranking accuracy staying very low. Two new diagnostic metrics are introduced to expose this calibration without comprehension failure mode.

LLMDeepSeekLinux kernel

CWE-TraceDirectional Failure IndexHierarchical Distance and Direction

AI Security, AppSec· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot

This arXiv paper introduces Knowledge Trap, a defense against model extraction attacks on commercial LLM APIs that lures attackers into querying a Honeypot Knowledge Graph (HKG) full of low-transferability knowledge via breadcrumb-guided exploration, wasting the attacker's query budget instead of blocking or perturbing outputs. Evaluated in medical and financial domains, it reduces surrogate model agreement by 6.2% on average while preserving accuracy for legitimate users, outperforming prior extraction defenses that degrade utility.

LLM

Knowledge TrapHoneypot Knowledge Graph

AI Security· Added 26 Sep 2026

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