arXiv AI
Jun 16
EffGen: Enabling Small Language Models as Capable Autonomous Agents
arXiv:2602. 00887v2 Announce Type: replace-cross Abstract: Most existing language model agentic systems today are built and optimized for large language models (e.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Jun 16
When RAG Hurts: Diagnosing and Mitigating Attention Distraction in Retrieval-Augmented LVLMs
arXiv:2602. 00344v2 Announce Type: replace-cross Abstract: While Retrieval-Augmented Generation (RAG) is one of the dominant paradigms for enhancing Large Vision-Language Models (LVLMs) on knowledge-based VQA tasks, recent work attributes RAG failures to insufficient attention towards the retrieved context, proposing to reduce the attention allocated to image tokens.
arXiv AI
Jun 16
CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions
arXiv:2512. 01095v2 Announce Type: replace-cross Abstract: We present CycliST, a novel benchmark dataset designed to evaluate Video Language Models (VLM) on their ability for textual reasoning over cyclical state transitions.
arXiv Machine Learning
Jun 16
AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models
arXiv:2602. 00482v2 Announce Type: replace Abstract: Reinforcement learning (RL)-based post-training for large language models (LLMs) is computationally expensive, as it generates many rollout sequences that frequently share long token prefixes.
arXiv AI
Jun 16
A Survey on Agentic Security: Applications, Threats and Defenses
arXiv:2510. 06445v3 Announce Type: replace-cross Abstract: LLM-based agents are now used throughout cybersecurity.
arXiv AI
Jun 16
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
arXiv:2505. 13986v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown promise for combinatorial optimization problems on graphs by learning heuristics that generalize across instances.
arXiv AI
Jun 16
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
arXiv:2606. 15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.
arXiv Machine Learning
Jun 16
Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.
arXiv Machine Learning
Jun 16
IGLU: The Integrated Gaussian Linear Unit Activation Function
arXiv:2603. 06861v2 Announce Type: replace Abstract: Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity.
arXiv Machine Learning
Jun 16
Descriptive versus Regulatory Uncertainty in Bounded Predictive Systems
arXiv:2605. 18909v2 Announce Type: replace Abstract: Any system that models the world under finite representational capacity must compress; any compression entails a prior; and the prior is the system's bias.
arXiv AI
Jun 16
From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents
arXiv:2606. 04990v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration.
arXiv AI
Jun 16
WorkflowPerturb: Calibrated Stress Tests for Evaluating Multi-Agent Workflow Metrics
arXiv:2602. 17990v2 Announce Type: replace Abstract: Multi-agent LLM systems that generate structured workflows from natural-language requests are now deployed in production across cloud automation, DevOps, and enterprise process orchestration.
arXiv AI
Jun 16
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
arXiv:2606. 16995v1 Announce Type: new Abstract: Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation.
arXiv AI
Jun 16
ACC: Compiling Agent Trajectories for Long-Context Training
arXiv:2605. 21850v2 Announce Type: replace-cross Abstract: Recent development of agents has renewed demand for long-context reasoning capacity of LLMs.
arXiv Machine Learning
Jun 16
Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests
arXiv:2605. 28734v2 Announce Type: replace-cross Abstract: A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon: a keylogger, ransomware, an exploit that runs as written.
arXiv Machine Learning
Jun 16
Conflict-Aware Federated Fine-Tuning of Large Language Models with Mixture-of-Experts
arXiv:2606. 15625v1 Announce Type: new Abstract: The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation.
arXiv AI
Jun 16
Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules
arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.
arXiv AI
Jun 16
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
arXiv:2604. 05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.
arXiv Machine Learning
Jun 16
Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning
arXiv:2606. 15333v1 Announce Type: cross Abstract: LLM unlearning has emerged as a cost-effective alternative to full retraining for removing hazardous knowledge from pretrained models while preserving general utility.
arXiv AI
Jun 16
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
arXiv:2602. 12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time.