AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsCan Agents Design Libraries for Agents?
arXiv:2609.36730v1 Announce Type: new Abstract: Agents increasingly build on code written by other agents, and they reimplement rather than reuse, growing the codebases later agents must work in. To measure how well agen…
- arXiv cs.AIresearchBiFE: Search-Efficient Discovery of CPU-Only Branching Policies via LLM-based Bi-Fidelity Evolution
arXiv:2609.36735v1 Announce Type: new Abstract: In branch-and-bound (B&B) for mixed-integer linear programming (MILP), branching variable selection critically impacts efficiency. Existing neural branching policies often…
- arXiv cs.AIagentsDistinguish or Homogenize: Last-Chance Policy Identification and Risk-Budgeted Recovery under Irreversible Resource Depletion
arXiv:2609.36741v1 Announce Type: new Abstract: Under irreversible resource depletion, an agent can spend resources to distinguish among latent fault models, or to change the system state so that the remaining models adm…
- arXiv cs.AIresearchSIPO: Unifying Reinforcement Learning with On-Policy Self-Distillation
arXiv:2609.36742v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a standard paradigm for improving large language models (LLMs) on various tasks, yet its sparse outcome rew…
- arXiv cs.AIagentsEASE: Behavior-Adaptive Skill Curation for Self-Evolving Agents
arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. However, existing learn…
- arXiv cs.AIresearchGeneralizable Lifelong Model Editing via Preference Optimization
arXiv:2609.36748v1 Announce Type: new Abstract: Knowledge editing enables rapid updates of specific factual knowledge in large language models (LLMs) without full retraining. However, more realistic scenarios call for a…
- arXiv cs.AIagentsEmergent Specialization in Populations of Self-Supervised Collaborative Vision Experts Without a Shared Gate or Cross-Agent Gradients
arXiv:2609.36770v1 Announce Type: new Abstract: Can a population of neural networks develop a useful division of labor without a shared gate or gradients between agents? We study a setting where each network has its own…
- arXiv cs.AIagentsCode4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenes
arXiv:2609.36777v1 Announce Type: new Abstract: Frontier coding agents can now write and execute code that authors 3D environments, but whether they reliably understand 3D structure and precisely control scene state rema…
- arXiv cs.AIresearchAperture: Merge-Consistent Rotary States for Compressed Tokens
arXiv:2609.36781v1 Announce Type: new Abstract: Token compression combines content from several positions, yet rotary position embeddings usually assign the merged token one coordinate. We ask what positional information…
- arXiv cs.AIresearchAI as a Compiler: Compiling Triton kernels without the Triton compiler
arXiv:2609.36800v1 Announce Type: new Abstract: Compiler backends are expensive to build and maintain as programming models, workloads, and accelerators evolve. We investigate whether large language models can replace th…
- arXiv cs.AIagentsUpliftMem: Learning Set-Level Uplift for Agent Memory Retrieval
arXiv:2609.36805v1 Announce Type: new Abstract: Large language model (LLM) agents reuse external memory to guide new tasks, but effective retrieval requires learning which memory sets improve execution. Such learning rel…
- arXiv cs.AIresearchCANTO: CAD-Native Transformer Operators for AI-Aided Engineering
arXiv:2609.36806v2 Announce Type: replace Abstract: Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models. Evaluating design cha…
- arXiv cs.AIresearchGeometry-Conditioned Fixed-Scaffold Encoders for Time-Warp Robust Sequence Retrieval
arXiv:2609.36809v1 Announce Type: new Abstract: Embedding-based retrieval is attractive for long sequence collections because each item can be encoded once and searched by nearest-neighbor ranking. The difficulty is that…
- arXiv cs.AIresearchCalibrate the Decisions That Change the Future: On-Policy Post-Training Quantization for Multimodal Large Language Models
arXiv:2609.36828v1 Announce Type: new Abstract: Post-training quantization (PTQ) lowers deployment cost for multimodal large language models, but calibration typically reconstructs fixed sequences with local objectives.…
- arXiv cs.AIagentsThe Default Trap: Rethinking Plan Evaluation in Tool-Using LLM Agents
arXiv:2609.36829v1 Announce Type: new Abstract: An executor can respond strongly to a change in a supplied plan's priority while showing a small change in the same information-selection probability when a default-aligned…
- arXiv cs.AIresearchWhere Does Staleness Accumulate? Pool Aware Effective Staleness Control for Asynchronous RL in LLM Post-Training
arXiv:2609.36830v1 Announce Type: new Abstract: Fully asynchronous reinforcement learning (RL) improves resource utilization in large language model post-training by overlapping rollout generation with policy optimizatio…
- arXiv cs.AIresearchARC-KV: Amortizing Anchor Search for Reconstruction-Based KV Cache Compaction
arXiv:2609.36835v1 Announce Type: new Abstract: Long-context large language model inference is bottlenecked by KV caches that grow linearly with sequence length. This burden is especially severe for long, reusable contex…
- arXiv cs.AIresearchAutomated Screw Planning for Reduced Pelvic Fractures Based on Statistical Shape Models and Deep Learning
arXiv:2609.36847v1 Announce Type: new Abstract: Percutaneous iliosacral screw fixation is an important minimally invasive treatment for unstable pelvic fractures. Because the sacroiliac region has complex anatomy and nar…
- arXiv cs.AIagentsWhen Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration
arXiv:2609.36855v1 Announce Type: new Abstract: Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful information or an incorr…
- arXiv cs.AIresearchIronLLM: Forging Compact Edge-Native Language Models for Real-Time Embodied Intelligence
arXiv:2609.36860v1 Announce Type: new Abstract: We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architecture with X-MTP, a lig…
- arXiv cs.AIagentsState Trace Rationale As Auxiliary Task in Reinforcement Learning
arXiv:2609.36867v1 Announce Type: new Abstract: We propose STRAT, an auxiliary task that trains deep reinforcement learning (RL) agents to predict a short textual trace of their own state. Inspired by human spatial navig…
- arXiv cs.AIagentsWEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
arXiv:2609.36887v1 Announce Type: new Abstract: Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component of a broader agentic i…
- arXiv cs.AIresearchBeyond Sub-Gaussian Detector Scores: Robust Weighted Profile-Loss Change Point Detection for Human-LLM Text Segmentation
arXiv:2609.36888v1 Announce Type: new Abstract: Mixed human-LLM documents require locating authorship transitions from detector scores whose reliability varies across text units. Existing weighted mean contrasts are vuln…
- arXiv cs.AIagentsHarness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents
arXiv:2609.36892v1 Announce Type: new Abstract: As the capabilities of large language models (LLMs) continue to advance, increasing attention is turning to how to translate their abilities into useful behavior. Personal…
- arXiv cs.AIresearchHorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL
arXiv:2609.36896v1 Announce Type: new Abstract: Recent advances in generative planning have made trajectory inpainting a promising approach to offline goal-conditioned reinforcement learning. However, these methods typic…
- arXiv cs.AIresearchSTAR-GRPO: Canonical Anchoring and Reliability-First Advantages against Representation-Dependent Reward Hacking
arXiv:2609.36900v1 Announce Type: new Abstract: Reward hacking occurs when policy optimization exploits a brittle reward interface or an overly permissive proxy objective, improving the training score without improving t…
- arXiv cs.AIagentsPrecogUI: Proactive GUI Agents via Pre-cognitive Simulation and Experience Retrieval
arXiv:2609.36923v1 Announce Type: new Abstract: Existing reactive Graphical User Interface (GUI) agents often fail in long-horizon, dynamic scenarios, where unexpected disturbances trigger attention-diverting and cascadi…
- arXiv cs.AIagentsNeuro-Symbolic Computer Use: Learning Reusable Policies for Reliable and Efficient Execution
arXiv:2609.36927v1 Announce Type: new Abstract: Many computer tasks recur: the same workflow runs many times, with new inputs and from different starting states. Current computer-use agents re-plan every step of every ru…
- arXiv cs.AIresearchLearn from the Gap: Differential-Aware Advantage Pruning with Adaptive Rollout Sampling for GRPO
arXiv:2609.36932v3 Announce Type: replace Abstract: Recently, Group Relative Policy Optimization (GRPO) and its variants have been developed for policy optimization and demonstrated notable performance gains. However, th…
- arXiv cs.AIresearchVLALight: A Vision-Language-Action Model for Traffic Signal Control
arXiv:2609.36934v1 Announce Type: new Abstract: Traffic signal control (TSC) is essential for improving urban mobility and reducing congestion. Although roadside cameras are widely deployed at signalized intersections an…
- arXiv cs.AIresearchCoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory
arXiv:2609.36935v2 Announce Type: replace Abstract: Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases. Recent…
- arXiv cs.AIagentsSCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents
arXiv:2609.36939v1 Announce Type: new Abstract: GUI agents automate tasks on digital devices by grounding language instructions in visual interfaces. Existing group-relative reinforcement learning improves GUI action pre…
- arXiv cs.AIresearchDual-Channel Robust Group-Relative Policy Optimization via Advantage and Sequence-Weight Estimation
arXiv:2609.36944v1 Announce Type: new Abstract: Group-relative policy optimization relies on reward-derived advantages and sequence-level likelihood weights, both of which can be sensitive to localized outliers. Extreme…
- arXiv cs.AIresearchREALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing
arXiv:2609.36984v1 Announce Type: new Abstract: Complex questions often require multi-hop reasoning that connects facts distributed across sources or distant regions of a long context through intermediate steps. Benchmar…
- arXiv cs.AIresearchCF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
arXiv:2609.36986v1 Announce Type: new Abstract: Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under hete…
- arXiv cs.AIresearchImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction
arXiv:2609.36996v1 Announce Type: new Abstract: Knowledge Graph Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missing links. However, th…
- arXiv cs.AIagentsCADOC: Cache-Aware Dynamic Object Context for Long-Horizon Agents
arXiv:2609.37012v1 Announce Type: new Abstract: For a long-horizon agent, context is the bottleneck: the history is resent with every request, the window caps task length, and reasoning degrades as the history grows. Rep…
- arXiv cs.AIagentsPhysics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization
arXiv:2609.37022v1 Announce Type: new Abstract: Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization. While classical queue…
- arXiv cs.AIresearchLanguage as the Interface: Foundation-Model Contrastive Learning Links Transcriptomes and Electrophysiology
arXiv:2609.37024v1 Announce Type: new Abstract: Integrating transcriptomic and electrophysiological data is essential for building multimodal foundation models for neuroscience. Patch-seq provides paired measurements of…
- arXiv cs.AIagentsAnyAct: Universal Action for Self-Evolving Agents
arXiv:2609.37025v1 Announce Type: new Abstract: As large language models (LLMs) advance, AI agents are increasingly deployed in open-world environments to tackle complex sequential tasks (e.g., document processing, cross…
- arXiv cs.AIresearchBeyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition
arXiv:2609.37027v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-tuning (FFT). Many Lo…
- arXiv cs.AIresearchFedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
arXiv:2609.37033v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneit…
- arXiv cs.AIresearchWatch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
arXiv:2609.37035v1 Announce Type: new Abstract: Streaming video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches model response timing…
- arXiv cs.AIagentsMatToolBench: Benchmarking Multimodal Agents in Real-World Materials Science Workflows
arXiv:2609.37053v1 Announce Type: new Abstract: Multimodal GUI agents have achieved impressive results on general software benchmarks, yet their ability to operate professional scientific software remains largely unexplo…
- arXiv cs.AIresearchACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
arXiv:2609.37054v2 Announce Type: replace Abstract: Ensuring the safety of reasoning large language models (LLMs) across languages is essential for their reliable deployment. However, when exposed to jailbreak attacks in…
- arXiv cs.AIresearchBreaking the Illusion of Review Reliability under Static Evaluation: SCOPE Fuzzing for LLM-based Scientific Reviewers
arXiv:2609.37097v1 Announce Type: new Abstract: The rapid growth of submissions and reviewing workload has accelerated the use of large language models (LLMs) in peer review. Prior studies suggest that LLM-based reviewer…
- arXiv cs.AIagentsLearning from Viable Failure Prefixes: Milestone Viability Potential Policy Optimization for Long-Horizon LLM Agents
arXiv:2609.37111v2 Announce Type: replace Abstract: Long-horizon LLM agents require reinforcement learning methods that can assign credit to intermediate decisions under sparse and delayed rewards. Existing group-based m…
- arXiv cs.AIagentsWhen Should Agents Check External State? Budgeting Observations for Stored Intentions
arXiv:2609.37125v3 Announce Type: replace Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition currently holds. C…
- arXiv cs.AIresearchSkillCome: Group Contrast Skill Optimization with Dual Memory
arXiv:2609.37128v1 Announce Type: new Abstract: Skill evolution improves the capabilities of large language models by analyzing trajectories generated under a given skill and modifying the skill accordingly. Existing app…
- arXiv cs.AIresearchTrain Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models
arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level supervision on the model's…