AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchDetecting Hallucination in LLMs: Tracing the Topological Signatures of Impaired Context Sharing
arXiv:2609.21096v1 Announce Type: new Abstract: In this work, we examine the topology of information flow patterns within attention graphs to effectively distinguish hallucinated from non-hallucinated responses. We analy…
- arXiv cs.AIresearchDecoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models
arXiv:2609.21113v2 Announce Type: replace Abstract: Fine-tuning has emerged as a widely adopted approach for adapting LLMs to a variety of downstream tasks. However, how it reshapes their internal mechanisms remains poor…
- arXiv cs.AIresearchTinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers
arXiv:2609.21139v1 Announce Type: new Abstract: Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers. TinyCeNN-LM introd…
- arXiv cs.AIresearchClinician-Grounded Quality Assurance for AI-Assisted Psychiatric Intake
arXiv:2609.21149v1 Announce Type: new Abstract: Before patients can use AI-assisted psychiatric intake systems, health systems need practical ways to routinely evaluate these tools against their clinical standards for qu…
- arXiv cs.AIagentsCan Agents Design Better Chips with a Higher Level Abstraction?
arXiv:2609.21157v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly being explored for chip design, but most existing approaches operate directly at RTL. We ask whether agents can design be…
- arXiv cs.AIagentsSpecOpt: Contact-Diff Reasoning for Agentic Molecule Optimization Toward Binding Specificity
arXiv:2609.21165v1 Announce Type: new Abstract: Off-target protein binding is a major source of adverse effects for small-molecule drugs, yet most structure-based molecular design methods focus on generating selective co…
- arXiv cs.AIresearchImplicit Rule Induction with Test-Time Task Embeddings in ARC-like Tasks
arXiv:2609.21181v1 Announce Type: new Abstract: The Abstraction and Reasoning Corpus and related benchmarks evaluate whether AI models can solve novel reasoning tasks, but often leave unclear whether success reflects inf…
- arXiv cs.AIagentsAI-GRACE: A Use-Case Operationalization Framework for Agentic AI: From Organizational Objectives and Obligations to Deployment Capabilities and Architecture
arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate, control, and observe…
- arXiv cs.AIresearchInformation-Gain Rewards over Diversity-Pruned Tests: GT-Anchored Verifier Co-Training for Reliable Code Generation
arXiv:2609.21208v1 Announce Type: new Abstract: Self-play methods that co-train a single language model as both coder and test author promise to move code-generation RL beyond fixed test suites, but they suffer from two…
- arXiv cs.AIresearchAbility-Residual Decoupled Modeling for Affective Cognitive Diagnosis
arXiv:2609.21214v1 Announce Type: new Abstract: Cognitive diagnosis infers students' concept mastery from response logs. However, students' responses are not determined by mastery alone: non-cognitive factors such as emo…
- arXiv cs.AIresearchA Fully Differentiable Neuro-Soft-Symbolic Framework for Perceptual Task Planning
arXiv:2609.21221v1 Announce Type: new Abstract: Perceptual planning tasks require two key capabilities: accurately perceiving uncertain scenes and planning valid action sequences following logical rules. Conventional met…
- arXiv cs.AIresearchCogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
arXiv:2609.21259v1 Announce Type: new Abstract: Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in w…
- arXiv cs.AIresearchPlaceReasoner-Beta: Reasoning-Driven Macro Placement and Benchmarking
arXiv:2609.21263v2 Announce Type: replace Abstract: Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optimize hand-crafted…
- arXiv cs.AIagentsEfficient Benchmarking in Production: A Study of an Evolving LLM Agent
arXiv:2609.21267v1 Announce Type: new Abstract: Production LLM agents are evaluated repeatedly as they evolve, but full agent benchmarks are costly to rerun. We study efficient recurring evaluation for a production analy…
- arXiv cs.AIagentsGameASG-Bench: Benchmarking Autonomous Software Generation for Game Development
arXiv:2609.21293v1 Announce Type: new Abstract: Autonomous software generation (ASG) aims to turn human requirements into executable applications, but delivering these applications does not necessarily establish that the…
- arXiv cs.AIagentsLEGIT: Credentialing Protocol for Trustworthy AI Agent Marketplaces
arXiv:2609.21325v1 Announce Type: new Abstract: Agentic marketplaces are emerging where AI agents with varying capabilities autonomously complete specialized tasks for buyers. A major challenge of such marketplaces is th…
- arXiv cs.AIresearchOffline Multimodal Large Language Models for Decision Support in Air Operations
arXiv:2609.21390v1 Announce Type: new Abstract: Air operations rely on complex rules, established procedures, and time-critical analysis under limited connectivity and strict security constraints. In such environments, a…
- arXiv cs.AIagentsDENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
arXiv:2609.21423v5 Announce Type: replace Abstract: Online agent deployments accumulate execution trajectories at massive scale and behavioral diversity, for which predefined annotation criteria hardly exist. Extracting…
- arXiv cs.AIresearchGVPO++: Group Variance Policy Optimization for LLM Post-Training and On-Policy Distillation
arXiv:2609.21432v1 Announce Type: new Abstract: Post-training plays a pivotal role in enhancing the reasoning capabilities and task-specific expertise of large language models (LLMs). Despite recent advances in post-trai…
- arXiv cs.AIagentsRisk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving
arXiv:2609.21470v2 Announce Type: replace Abstract: Conventional end-to-end driving systems model the environment with sparse objects and lane elements. While efficient, this paradigm discards planning-critical informati…
- arXiv cs.AIagentsDriving on Registers, Reasoning on Risk: Risk-Aware Occupancy for Register-Based End-to-End Autonomous Driving
arXiv:2609.21486v1 Announce Type: new Abstract: Multimodal trajectory prediction improves behavioral coverage in end-to-end autonomous driving, but existing methods remain limited by sparse scene representations. Incompl…
- arXiv cs.AIresearchLogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers
arXiv:2609.21492v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely rely on outcome-based…
- arXiv cs.AIresearchPolyBridgeBench: Benchmarking Multimodal LLMs for Physics-Grounded Bridge Design
arXiv:2609.21493v1 Announce Type: new Abstract: Multimodal large language models, or MLLMs, perform well at visual understanding and structured generation, yet these capabilities do not establish whether an engineering d…
- arXiv cs.AIresearchThe Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models
arXiv:2609.21509v2 Announce Type: replace Abstract: When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How much tree-structur…
- arXiv cs.AIagentsLearning-to-Optimize as the Missing Architectural Layer of AI-Native Networks
arXiv:2609.21519v1 Announce Type: new Abstract: Artificial Intelligence (AI) is becoming a fundamental design principle of future AI-native communication networks, enabling autonomous resource management, adaptive contro…
- arXiv cs.AIresearchDual-Interest Sequential Product Recommendation With Multi-Granular SSM
arXiv:2609.21548v1 Announce Type: new Abstract: Sequential recommendation aims to predict the next item a user will interact with based on their historical behavior. Advances in Transformers have significantly improved s…
- arXiv cs.AIresearchBeyond Accuracy: Centroid-Guided Contrastive Loss for Structured Fraudulent Job Posting Detection
arXiv:2609.21599v1 Announce Type: new Abstract: Fraudulent job posting detection aims to identify job advertisements that are corrupted either through fake content, misleading information, or negative intent, disrupting…
- arXiv cs.AIresearchReducing Barriers to Academic Support: Evaluating a Course-Specific RAG System for Addressing Help-Seeking Disparities in Higher Education
arXiv:2609.21600v1 Announce Type: new Abstract: Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutors, while others hesi…
- arXiv cs.AIresearchCalibrating Teacher--Student Discrepancy for On-Policy Distillation
arXiv:2609.21619v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student. However, this discrepanc…
- arXiv cs.AIresearchOne Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction
arXiv:2609.21626v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for enterprise information extraction (IE), where the same document must be reorganized differently for each user. Ex…
- arXiv cs.AIresearchAccelerating Dense LLMs via L0-regularized Mixture-of-Experts
arXiv:2609.21672v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but suffer from slow and costly inference. Existing acceleration methods often lead to noticeable performance degrad…
- arXiv cs.AIresearchGUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
arXiv:2609.21677v2 Announce Type: replace Abstract: Recent advances in large reasoning models (LRMs) have made machine unlearning more challenging, as protected facts or unsafe rationales may surface in intermediate chai…
- arXiv cs.AIresearchListen Before You Speak: Response Planning from Listener Facial Reactions for Conversational Speech Generation
arXiv:2609.21683v1 Announce Type: new Abstract: Conversational speech depends on dialogue context and the listener's immediately preceding behavior. We propose ReACT-TTS, a two-stage framework that uses a one-second pre-…
- arXiv cs.AIresearchWorld Modeling in Transformers
arXiv:2609.21748v1 Announce Type: new Abstract: Behavioral failures can make a transformer appear to lack a world model even when it has learned faithful representations of its environment. We demonstrate this in TaxiGPT…
- arXiv cs.AIresearchECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction
arXiv:2609.21755v1 Announce Type: new Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and electrocardiograms (…
- arXiv cs.AIresearchLLM-Generated Feature Pools for Time Series Anomaly Detection
arXiv:2609.21801v1 Announce Type: new Abstract: We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts a small pool of sta…
- arXiv cs.AIresearchMIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
arXiv:2609.21811v1 Announce Type: new Abstract: Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid exte…
- arXiv cs.AIresearchEnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
arXiv:2609.21841v2 Announce Type: replace Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valuable tasks, yet…
- arXiv cs.AIresearchAutoRecLab: Describe the Experiment, Get the Code!
arXiv:2609.21863v1 Announce Type: new Abstract: Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We pr…
- arXiv cs.AIagentsWhat Should We Ask Next? Retrieval-Aware Question Learning for Interactive ReID
arXiv:2609.21924v2 Announce Type: replace Abstract: Interactive retrieval with partial evidence constitutes a sequential information-acquisition problem: an agent must choose questions that acquire useful evidence for th…
- arXiv cs.AIagentsAutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
arXiv:2609.21940v1 Announce Type: new Abstract: Long-term memory is essential for large language model (LLM) agents to maintain consistency and personalization over extended interactions. Existing memory systems typicall…
- arXiv cs.AIresearchLearning Cardiac Features: ECG Biometrics Across Time and~Exercise
arXiv:2609.21962v1 Announce Type: new Abstract: Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authentication, this para…
- arXiv cs.AIresearchA Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal
arXiv:2609.21996v2 Announce Type: replace Abstract: Large language models can hold knowledge they do not report. A model may sandbag on a capability evaluation, or answer against what it internally knows, and its outputs…
- arXiv cs.AIagentsCodeMidas: Scaling Agentic Coding RL Environments from Code Itself
arXiv:2609.22068v1 Announce Type: new Abstract: Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, whil…
- arXiv cs.AIagentsDesigner-RSI: Evolving Procedural Memory from User Traffic for Agentic Graphic Design
arXiv:2609.22086v1 Announce Type: new Abstract: Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable p…
- arXiv cs.AIresearchResNLS: An Improved Model for Stock Price Forecasting
arXiv:2312.01020v3 Announce Type: cross Abstract: Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees…
- arXiv cs.AIresearchdSTAR: Straggler Tolerant and Byzantine Resilient Distributed SGD
arXiv:2412.07151v1 Announce Type: cross Abstract: Distributed model training needs to be adapted to challenges such as the straggler effect and Byzantine attacks. When coordinating the training process with multiple comp…
- arXiv cs.AIresearchA Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests
arXiv:2511.16923v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution but suffers from pervasive dropout events that obscure biological signals.…
- arXiv cs.AIresearchMaking Latent Evolution Explicit: Operator-Structured Transitions for World Action Models
arXiv:2608.27259v1 Announce Type: cross Abstract: World Action Models (WAMs) augment robot policies by predicting how task-relevant scene states may evolve under interaction. Recent WAMs increasingly perform such predict…
- arXiv cs.AIresearchReinforcement learning for post-coronagraphic wavefront control
arXiv:2609.20880v1 Announce Type: cross Abstract: Direct imaging of exoplanets is limited by the extreme contrast between the star and the planets, which is mitigated using a coronagraph. However, optical aberrations cau…