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arXiv:2604.06389v1 Announce Type: new Abstract: Uncertainty estimation for reasoning language models remains difficult to deploy in practice: sampling-based methods are computationally expensive, whil

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Model Releases

SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training

DGX agent

arXiv:2604.06900v1 Announce Type: cross Abstract: The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor wea

model-releasesarxiv-cs-ai

10 Apr 2026

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training

arXiv:2604.07754v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) raises significant ethical and safety concerns. While LLM alignment techniques are adopted to improve m

safetyarxiv-cs-cl

The Geometry of Forgetting

arXiv:2604.06222v1 Announce Type: cross Abstract: Why do we forget? Why do we remember things that never happened? The conventional answer points to biological hardware. We propose a different one: ge

Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing

arXiv:2604.08401v1 Announce Type: cross Abstract: In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However

BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks

arXiv:2610.22256v1 Announce Type: new Abstract: Reducing memory use in large language model (LLM) training is an ongoing challenge. Activation checkpointing saves memory by recomputation, while lossy

Local Ai

Can Capability Enhancement be Localized?

arXiv:2610.21294v1 Announce Type: new Abstract: Transformer-based language models have shown strong capabilities across many tasks and domains. However, prior work shows that, for a given task, not al

local-aiarxiv-cs-lg

Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring

arXiv:2610.22187v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs. How

researcharxiv-cs-ai

CLUE: Uncovering Social Context for Pragmatic Understanding

arXiv:2610.22167v1 Announce Type: new Abstract: Understanding a conversation requires connecting what is said to the speakers' relationships, expectations, and earlier exchanges. Prior work shows that

Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework

arXiv:2602.05708v2 Announce Type: replace-cross Abstract: Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensi

Decoding What iOS Location Services Send to Apple: An LLM Is an Unreliable Analyst but a Useful Toolmaker

arXiv:2610.21597v1 Announce Type: cross Abstract: Reverse engineering the data formats used by mobile software is skilled and slow, and using AI to assist with it is attractive. We evaluate this for t

Dexterity from Supervised Play

arXiv:2610.22340v1 Announce Type: cross Abstract: How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play

safetyarxiv-cs-ai

Digital Typhoon Dataset V3: Benchmarking for Multi-spectral Extension and Analyzing Systematic Bias in Intensity Regression

model-releasesarxiv-cs-cv

arXiv:2610.21654v1 Announce Type: new Abstract: This paper presents the Digital Typhoon Dataset V3, a new version of the longest typhoon satellite image dataset spanning nearly 50 years, designed to b

Agents

DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding

arXiv:2610.21327v1 Announce Type: new Abstract: Multimodal long-document question answering requires models to seek sparse evidence across pages and modalities, understand heterogeneous document conte

agentsarxiv-cs-ai

ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases

arXiv:2610.21368v1 Announce Type: cross Abstract: With the expansion of pre-trained language model usage in recent years, the importance of datasets for performing tasks in specialized domains has sig

researcharxiv-cs-ai

FANTF: Fuzzy attention network based transformers for time series analysis

arXiv:2504.00070v2 Announce Type: replace Abstract: This paper introduces Fuzzy Attention Network-Based Transformer (FANTF), an approach for time series forecasting, classification, and anomaly detect

FGSS-BO: Feedback-Guided Hypothesis Management for LLM-Based Bayesian Optimization

arXiv:2610.21246v1 Announce Type: new Abstract: Large language model (LLM)-guided Bayesian optimization (BO) uses past observations to guide search, but proposed directions and their revision can rema

FIRM: Fisher-Informed Retention Merging for Federated LLM Unlearning

arXiv:2610.21720v1 Announce Type: cross Abstract: Federated LLM unlearning addresses targeted knowledge removal when forget and retain data remain distributed across private clients and are inaccessib

Gibbs-Gap Policy Optimization for Off-Policy RL with Verifiable Rewards

arXiv:2610.20814v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards often trains language models on responses generated by a different, possibly stale behavior policy. Cor

INFUSE: A Unified Surface Benchmark for Indirect Prompt Injection in Agentic Systems

arXiv:2610.20776v1 Announce Type: cross Abstract: LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and int

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

arXiv:2610.22273v1 Announce Type: cross Abstract: Reasoning over long document contexts remains a challenge for large language models (LLMs). Standard retrieval methods are inherently reactive, bottle

Not Just a Subject: Capturing Adaptive Scene-Specific Variance for Visual Storytelling

model-releasesarxiv-cs-cv

arXiv:2610.22320v1 Announce Type: new Abstract: Recent advancements in diffusion models have significantly improved text-to-image generation, enabling their application to visual storytelling, where c

Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation

arXiv:2602.18891v2 Announce Type: replace-cross Abstract: Advances in large language models (LLMs) are rapidly transforming scientific work, yet empirical evidence on how these systems reshape researc

safetyarxiv-cs-ai

Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble

arXiv:2610.21134v1 Announce Type: new Abstract: Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous,

researcharxiv-cs-lg

QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery

arXiv:2610.21333v1 Announce Type: new Abstract: Natural language messages often contain quasi-identifiers that are weak in isolation but can jointly reveal an individual's private attributes. We call

Agents

Reliable Knowledge Acquisition for Continual Learning Agents

arXiv:2610.21739v1 Announce Type: cross Abstract: As agents powered by foundation models move into open-ended environments, continual learning from experience becomes essential. For agents built on mo

agentsarxiv-cs-ai

SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation

model-releasesarxiv-cs-cv

arXiv:2610.22264v1 Announce Type: new Abstract: Reinforcement learning (RL) enables language models to improve text-to-image (T2I) generation by rewriting prompts for a frozen image generator. Existin

Self-Calibrated Subspace Robust PCA for Parameter-Free Removal of Mixed Noise and Line Stripes in Hyperspectral Images

arXiv:2610.21896v1 Announce Type: new Abstract: Hyperspectral images (HSIs) are often corrupted by Gaussian noise, impulses and stripes whose intensities vary across bands. The representative coeffici

SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations

arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints,

SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills

arXiv:2610.20926v1 Announce Type: new Abstract: Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on t

Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors

arXiv:2610.20826v1 Announce Type: new Abstract: We investigate whether per-layer, per-task-phase (prompt/reasoning/response) expert-routing statistics in a Mixture-of-Experts (MoE) language model can

The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance

arXiv:2610.21746v1 Announce Type: cross Abstract: Many collective-action systems are neither fully decentralized nor fully centralized: agents voluntarily organize into institutions that share organiz

Verdict: Encode Once, Decide Many

arXiv:2610.20778v1 Announce Type: cross Abstract: Automating organizational work means answering many small typed questions about one piece of state (does this email need a reply, how urgent is it, wh

VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility

arXiv:2610.21330v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for election information, yet little is known about how external web content shapes the political i

Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL

arXiv:2610.20898v1 Announce Type: cross Abstract: Reinforcement learning with self-generated labels, such as majority vote over a model's own samples, often recovers most of the gain of training with

researcharxiv-cs-ai

Hardware

ZenFlow: Enabling Stall-Free Offloading for LLM Training

arXiv:2505.12242v4 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) often exceeds GPU memory limits, prompting systems to offload model states to CPU memory. However, ex

hardwarearxiv-cs-lg

ZoomCap: Evidence-Routed Visual Zooming for Recoverable Visual Information Transfer

arXiv:2610.21320v1 Announce Type: cross Abstract: Detailed captions transfer visual information into vision-language model supervision, but quality is easily conflated with length: long single-pass ou

A Mode-Unified, Continuous, and Biomimetic Control Strategy of a Robotic Lower Limb Prosthesis

model-releasesarxiv-cs-ro

arXiv:2610.19183v1 Announce Type: new Abstract: Existing biomimetic lower-limb prosthesis controllers are commonly designed for discrete locomotion modes, requiring separate parameterizations for leve

Hardware

AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents

arXiv:2610.20474v1 Announce Type: new Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, b

hardwarearxiv-cs-cl

AlignAudio: A Unified Dual-Alignment Framework for Noise-Robust Speech-Conditioned Generation

arXiv:2610.14697v1 Announce Type: cross Abstract: Speech-conditioned generation maps speech signals to target audio outputs. Prior end-to-end speech-conditioned generative models achieve high fidelity

safetyarxiv-cs-ai

Are We Really Measuring Forgetting? Pitfalls in Evaluating Machine Unlearning for Multimodal LLMs

arXiv:2610.19613v1 Announce Type: new Abstract: As machine unlearning for multimodal large language models (MLLMs) rapidly advances, rigorous evaluation of its effectiveness becomes increasingly impor

Tutorials

Attention Sinks in VLMs Are Borrowed, Not Seen

arXiv:2610.15855v1 Announce Type: new Abstract: We study how to detect and mitigate visual attention sinks in vision-language models (VLMs). Visual attention sinks are image tokens that absorb a large

tutorialsarxiv-cs-ai

au-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions

arXiv:2610.19560v1 Announce Type: cross Abstract: In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users. Y

Benchmarking 3D Reconstruction for Under-Ice Robotic Perception in Arctic Environments

model-releasesarxiv-cs-ro

arXiv:2610.19097v1 Announce Type: new Abstract: As the Arctic transitions toward a seasonally ice-free state, capturing the complex morphology of submerged sea ice is crucial for advancing models of o

Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization

arXiv:2610.19084v1 Announce Type: cross Abstract: Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically. However, its effectiveness is usually measure

Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate

arXiv:2610.18240v1 Announce Type: new Abstract: Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. How

Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning

arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing me

Beyond Perfect Teammates: Multi-Agent Collaboration with Unreliable Colleagues

arXiv:2610.16117v2 Announce Type: new Abstract: LLM-based multi-agent collaboration is increasingly used to solve complex tasks by dividing work among specialized agents and integrating complementary