AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMUSE: Benchmarking Assembly Structure and Interface Geometry in Multi-Component Text-to-CAD Generation
arXiv:2605.28579v3 Announce Type: replace Abstract: Large language models (LLMs) have recently advanced text-driven 3D generation, yet generating CAD models that satisfy practical design requirements remains challenging.…
- arXiv cs.AIresearchVLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing
arXiv:2605.30117v3 Announce Type: replace Abstract: Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Trace, a progressiv…
- arXiv cs.AIresearchDistilling LLM Feedback for Lean Theorem Proving
arXiv:2605.30861v2 Announce Type: replace Abstract: Post-training for reasoning models typically combines supervised fine-tuning with reinforcement learning from verifiable rewards, most commonly with GRPO. However, this…
- arXiv cs.AIresearchFAM-Bench: Benchmarking Condition-Aware Food-as-Medicine Reasoning in Vision-Language Models
arXiv:2605.31410v2 Announce Type: replace Abstract: Food AI can recognize dishes, ingredients, and nutrients. However, these abilities do not show whether a dish is suitable for a person with a specific health condition.…
- arXiv cs.AIresearchEvidence Acquisition and Stopping via Lattice Distillation
arXiv:2606.09433v2 Announce Type: replace Abstract: Sequential evidence acquisition requires deciding what to observe and when to stop from partial information. We introduce a complete-record view of policy supervision:…
- arXiv cs.AIresearchFrame-Conditioned Moral Computation in LLaMA 3.1-8B-Instruct: An Exploratory Mechanistic Interpretability Audit of Ethical Reasoning
arXiv:2606.15507v2 Announce Type: replace Abstract: This exploratory observational study uses the Transluce platform to analyze the internal activity of Llama 3.1-8B-Instruct on 54 moral prompts in four batteries: dilemm…
- arXiv cs.AIresearchScientific discovery as meta-optimization: a combinatorial optimization case study
arXiv:2606.26728v2 Announce Type: replace Abstract: Scientific discovery is fundamentally an optimization problem, defined by a vast ``state space'' of theories and experiments, and an evaluation criterion based on quali…
- arXiv cs.AIagentsDiagnosing Task Insensitivity in Language Agents
arXiv:2606.26918v2 Announce Type: replace Abstract: Language agents select actions based on task descriptions, environmental observations, and interaction histories. We investigate whether task requirements reliably cons…
- arXiv cs.AIresearchArena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist
arXiv:2606.31711v2 Announce Type: replace Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models. Existing fait…
- arXiv cs.AIresearchHarnessing Textual Refusal Directions for Multimodal Safety
arXiv:2606.31876v3 Announce Type: replace Abstract: Safety in Multimodal Large Language Models (MLLMs) depends on unsafe multimodal data for post-training alignment or activation steering. However, such data is hard to c…
- arXiv cs.AIagentsJanus: a Playground for User-Involved Agentic Permission Management
arXiv:2607.01510v2 Announce Type: replace Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should…
- arXiv cs.AIresearchCan We Trust Item Response Theory for AI Evaluation?
arXiv:2607.15190v3 Announce Type: replace Abstract: AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate capabilities, rank systems, select informative e…
- arXiv cs.AIresearchNeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning
arXiv:2607.15776v3 Announce Type: replace Abstract: OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and b…
- arXiv cs.AIresearchMulti-Objective Structured Pruning of LLMs for Latency and Model Size Optimization
arXiv:2607.22583v3 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved widespread adoption because of their strong reasoning and query-response capabilities. However, deploying them in embedded an…
- arXiv cs.AIresearchCAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics
arXiv:2607.23258v3 Announce Type: replace Abstract: Calcium imaging has enabled large-scale recordings of neural population activity across diverse animals, laboratories, and experimental paradigms. However, these datase…
- arXiv cs.AIresearchDefense via Consensus: Mitigating Subliminal Learning through Multi-Teacher Distillation
arXiv:2607.23394v2 Announce Type: replace Abstract: Fine-tuning can introduce unwanted behaviors through deliberate poisoning or unexpected generalization from seemingly benign data. Because these effects may be difficul…
- arXiv cs.AIresearchWIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
arXiv:2607.28418v2 Announce Type: replace Abstract: Pruning is a promising approach for improving the efficiency of large language models (LLMs). Static structured pruning is hardware-friendly, but its input-agnostic com…
- arXiv cs.AIagentsADIAS: Automated Design of Interactive Agentic Systems
arXiv:2608.06410v2 Announce Type: replace Abstract: Self-evolving agents can improve without updating model weights by iteratively optimizing the harness based on evaluation feedback. Existing methods are largely candida…
- arXiv cs.AIagentsCausalNav: Reliability-Certified Causal World Models for Control under Physical-Parameter Shift
arXiv:2608.07809v2 Announce Type: replace Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong. We study both halves of that req…
- arXiv cs.AIresearchWhen the Judge Should Not Decide: Evidence-Locked, Non-Compensatory Selection Bounds LLM-Judge Failure in Reasoning Pipelines
arXiv:2608.07813v2 Announce Type: replace Abstract: An LLM judge deployed inside a reasoning pipeline does not merely measure quality, it decides which answer ships. We show that the cost of that decision depends less on…
- arXiv cs.AIresearchVERDI:Verified Contradictions for Continual World-Model Optimization
arXiv:2608.09537v2 Announce Type: replace Abstract: Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence. However, optimizing a pretrained world model toward a user-spe…
- arXiv cs.AIresearchThe edge-based contiguous p-median problem: exact formulations and strengthening inequalities
arXiv:2608.11230v2 Announce Type: replace Abstract: This paper introduces the edge-based contiguous p-median (ECpM) problem, which partitions the roads in a network into a given number of compact and contiguous territori…
- arXiv cs.AIresearchBuy the Rumor, Sell the News: When Is News Priced In?
arXiv:2608.14014v2 Announce Type: replace Abstract: Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both are empirical claims a…
- arXiv cs.AIagentsToward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement
arXiv:2608.14590v3 Announce Type: replace Abstract: Large Language Model (LLM) agents take irreversible real-world actions, yet no system gives formally grounded, task-level safety guarantees for their plans. In a PRISMA…
- arXiv cs.AIresearchWhen Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
arXiv:2608.15592v3 Announce Type: replace Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput. Output length predi…
- arXiv cs.AIagentsBreak It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
arXiv:2608.20274v2 Announce Type: replace Abstract: Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced skills may tran…
- arXiv cs.AIagentsMobilePA-Bench: Benchmarking Mobile Planner Agents on Complex Real-World Tasks
arXiv:2608.23035v3 Announce Type: replace Abstract: Mobile planning requires a central agent to coordinate tools, persistent user context, reusable procedures, and specialized sub-agents while adapting to execution feedb…
- arXiv cs.AIresearch6.5% of the Neuro-Symbolic Literature Can Be Reproduced from Its Published Artifacts, a Six-Stage Audit Framework and First Instantiation
arXiv:2608.26236v2 Announce Type: replace Abstract: We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain, and instantiate…
- arXiv cs.AIresearchOmni-Interactive Universal Embedder
arXiv:2608.27044v2 Announce Type: replace Abstract: Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruct…
- arXiv cs.AIagentsCascade Failure Attribution for Multi-Agent Systems
arXiv:2608.29646v2 Announce Type: replace Abstract: Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to execution failu…
- arXiv cs.AIagentsLLM-Driven Autonomous Vehicles Can Inherit Human Driver Biases in Pedestrian Yielding: Results and Implications From a New Benchmark
arXiv:2609.00192v2 Announce Type: replace Abstract: Public trust in Autonomous Vehicles (AVs) may depend not only on technical success but also on the fairness of their decision making. While a recent trend in AV researc…
- arXiv cs.AIresearchMultimodal Resource-Exhaustion Attacks on Vision-Language Models via Joint Pixel-Prompt Optimization
arXiv:2609.05889v2 Announce Type: replace Abstract: Resource-exhaustion attacks against autoregressive vision-language models (VLMs) typically assume unimodal threat models, treating the image branch as the primary optim…
- arXiv cs.AIagentsMemForest: Efficient Agent Memory Management via EventTree Partitioning and Progressive Merging
arXiv:2609.08273v2 Announce Type: replace Abstract: Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuously accumulated…
- arXiv cs.AIresearchApplication of curiosity driven exploration methods for hardware interference identification
arXiv:2609.08729v2 Announce Type: replace Abstract: The transition from single-core to multi-core architectures in safety-critical embedded systems introduces significant challenges due to inter-core interference caused…
- arXiv cs.AIresearchDifferential Functionalism: Jacobians Shape the Structure of Phenomenal Experience
arXiv:2609.09306v3 Announce Type: replace Abstract: This paper investigates the hypothesis that the first-order structure of physical interactions (Jacobians) shapes the structure of phenomenal experience. Two measures o…
- arXiv cs.AIagentsMulti-Agent Agentic Graph Learning via Structural Signatures
arXiv:2609.09565v2 Announce Type: replace Abstract: Agentic graph learning (AGL) has recently achieved promising results on graph reasoning tasks, where an agent powered by a large language model (LLM) sequentially sampl…
- arXiv cs.AIresearchHow Good Are Frontier Models at Physics? Expert Re-Grading Reveals Broken Evaluations and Near-Saturation of Leading Benchmarks
arXiv:2609.13009v2 Announce Type: replace Abstract: Low reported scores on leading physics benchmarks, including those featured in the Artificial Analysis Intelligence Index (2026), suggest that frontier language models…
- arXiv cs.AIresearchGradient Boosting Reinforcement Learning
arXiv:2407.08250v3 Announce Type: replace-cross Abstract: We present Gradient Boosting Reinforcement Learning (GBRL), a framework that adapts the strengths of gradient boosting trees (GBT) to reinforcement learning (RL)…
- arXiv cs.AIresearchMitigating Data Reconstruction Attacks in Federated Learning: Conditions, Bounds, and Theoretical Guarantees
arXiv:2407.16735v2 Announce Type: replace-cross Abstract: Privacy attacks, particularly data reconstruction attacks, pose significant challenges to federated learning despite its promise in privacy-preserving collaborati…
- arXiv cs.AIresearchForecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
arXiv:2407.19564v2 Announce Type: replace-cross Abstract: Recent advancements in motion forecasting have been significantly accelerated by self-supervised pre-training techniques. However, current methods typically rely…
- arXiv cs.AIresearchCompetition and Diversity in Generative AI
arXiv:2412.08610v4 Announce Type: replace-cross Abstract: Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use o…
- arXiv cs.AIresearchReal Faults in Deep Learning Fault Benchmarks: How Real Are They?
arXiv:2412.16336v2 Announce Type: replace-cross Abstract: As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults with…
- arXiv cs.AIresearchPDB: Not All Drivers Are the Same -- A Personalized Dataset for Understanding Driving Behavior
arXiv:2503.06477v2 Announce Type: replace-cross Abstract: Driving behavior varies across individuals, yet most existing driving datasets do not preserve driver identity or control for factors that may affect driving styl…
- arXiv cs.AIresearchTestDG: Test-time Domain Generalization for Continual Test-time Adaptation
arXiv:2504.04981v4 Announce Type: replace-cross Abstract: This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preserving previousl…
- arXiv cs.AIagentsGaussTrap: Stealthy Backdoor Attacks on 3D Gaussian Splatting for Targeted Scene Misperception
arXiv:2504.20829v2 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful paradigm for real-time scene representation and novel view synthesis, gaining traction in safety-c…
- arXiv cs.AIresearchStudent Perspectives on the Benefits and Risks of AI in Education
arXiv:2505.02198v3 Announce Type: replace-cross Abstract: The use of chatbots equipped with artificial intelligence (AI) in educational settings has increased in recent years, showing potential to support teaching and le…
- arXiv cs.AIagentsData-driven multi-agent modelling of calcium interactions in cell culture: PINN vs Regularized Least-squares
arXiv:2505.20327v2 Announce Type: replace-cross Abstract: Data-driven discovery of dynamics in biological systems allows for better observation and characterization of processes, such as calcium signaling in cell culture…
- arXiv cs.AIagentsThe Feasibility of Topic-Based Watermarking on Academic Peer Reviews
arXiv:2505.21636v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated into academic workflows, with many conferences and journals permitting their use for tasks such as langua…
- arXiv cs.AIresearchTo Trust Or Not To Trust Your Vision-Language Model's Prediction
arXiv:2505.23745v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated strong capabilities in aligning visual and textual modalities, enabling a wide range of applications in multimodal…
- arXiv cs.AIresearchUNO: Unlearning via Orthogonalization in Generative models
arXiv:2506.04712v3 Announce Type: replace-cross Abstract: As generative models become increasingly powerful and pervasive, the ability to unlearn specific data due to privacy concerns, legal requirements, or harmful cont…