AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchVD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection
arXiv:2609.34949v1 Announce Type: new Abstract: Few-shot visual anomaly detection is fundamentally a visual comparison task, requiring fine-grained inspection of a query against normal references. Many recent methods bas…
- arXiv cs.AIresearchAX is the New AEO
arXiv:2609.34951v3 Announce Type: replace Abstract: In 2023, AI models answered from training data and hallucinated when it ran out, and businesses were told to seed that knowledge. Models' training knowledge has since g…
- arXiv cs.AIresearchProofLoom: Proof-Obligation-Driven Theory Construction for Autoformalizing Research-Level Stochastic Optimization
arXiv:2609.34960v1 Announce Type: new Abstract: Formalizing research-level stochastic optimization in Lean requires both an algorithm model and domain theory connecting foundational libraries to convergence proofs. Revis…
- arXiv cs.AIresearchSafe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks
arXiv:2609.34966v1 Announce Type: new Abstract: Greenhouse climate control balances economic return with maintaining temperature, humidity and CO2 within crop-adapted growth ranges. Conventional reinforcement learning (R…
- arXiv cs.AIresearchFrom One-Shot Generation to Incremental Music Composition: Adapting a General-Purpose Instruction LLM for Persistent Symbolic Editing
arXiv:2609.34994v1 Announce Type: new Abstract: Most music-generation systems are still framed and evaluated primarily as producers of complete outputs, whereas composition often proceeds through successive revisions to…
- arXiv cs.AIresearchAutomated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting
arXiv:2609.35013v2 Announce Type: replace Abstract: The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (…
- arXiv cs.AIresearchTermJudge: A Document-Level Metric Judging, Not Counting, Terminology in Machine Translation Evaluation
arXiv:2609.35017v1 Announce Type: new Abstract: Existing automatic metrics for evaluating terminological use in machine translation (MT) penalise any divergence from a fixed reference, conflating translation errors with…
- arXiv cs.AIresearchPersona Following Is Not Selective Control: The Neutrality Gap in LLM User Simulation
arXiv:2609.35036v1 Announce Type: new Abstract: Persona prompting is widely used to construct user simulations with large language models (LLMs), yet it relies on a largely untested assumption: specifying one user attrib…
- arXiv cs.AIresearchWhat Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pages
arXiv:2609.35077v1 Announce Type: new Abstract: Production large language models retrieve and cite web pages alongside generated answers, yet the page-level features that predict citation frequency remain poorly characte…
- arXiv cs.AIresearchCan Generative AI Automate Data Extraction for Meta-Analysis? A Case Study on Intercropping Research
arXiv:2609.35089v2 Announce Type: replace Abstract: Meta-analysis is the synthesis of information from multiple sources to arrive at an overarching conclusion. There is a large need for meta-analysis in agricultural rese…
- arXiv cs.AIresearchDoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
arXiv:2609.35107v2 Announce Type: replace Abstract: We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evide…
- arXiv cs.AIresearchUsing Context Is Not Enough: Test-Time Training for Personalized Reward Modeling
arXiv:2609.35109v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) aligns large language models (LLMs) with human preferences, yet most pipelines learn a single reward model that overlooks…
- arXiv cs.AIresearchSol-H3: Recursive Self-Improvement for MiniMax-H3 Inference Acceleration on Sol-Engine across Cloud and Edge
arXiv:2609.35110v1 Announce Type: new Abstract: Video diffusion models are rapidly scaling and exhibiting enhanced generation capabilities. Among these recent advancements, MiniMax-H3 stands out as a highly capable, prod…
- arXiv cs.AIresearchDuplexCadence: Exact State and Execution from a Speech Model's Declared Timelines
arXiv:2609.35115v1 Announce Type: new Abstract: Full-duplex speech models support streaming interaction that listens and speaks at the same time. Serving them is governed by a strict, repeating deadline: conversation adv…
- arXiv cs.AIresearchTool Mediation Alters Refusal Mechanisms in Large Language Models
arXiv:2609.35117v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed with access to external tools, yet harmful tool-mediated interactions are less likely to be refused when compared to…
- arXiv cs.AIresearchFONDANT: Strong and Best-Effort Planning via Antichains
arXiv:2609.35160v1 Announce Type: new Abstract: A classical solution concept in fully observable nondeterministic (FOND) planning, is the strong policy (aka winning strategy in the closely related area of reactive synthe…
- arXiv cs.AIresearch5W1H+Which: Context-Valid Semantic Indexing with Progressive Ontology Binding
arXiv:2609.35184v2 Announce Type: replace Abstract: Semantic indexing must preserve reusable source information without committing too early to one business schema, while providing checkable premises for reasoning. We pr…
- arXiv cs.AIresearchBeneath the Tokens: A Performance Engineering Study of Multi-Token Prediction in GPU-Accelerated LLM Inference
arXiv:2609.35188v1 Announce Type: new Abstract: Autoregressive large language model inference repeatedly invokes the target model to generate one token at a time, making generation sensitive to GPU memory movement and se…
- arXiv cs.AIresearchImprint Reader: From Weight-Update Readout to Behavioral Intervention
arXiv:2609.35261v1 Announce Type: new Abstract: As language models take a growing role in AI development, a natural aspiration is for them to reflect on their own learning process, as humans do, and use that reflection t…
- arXiv cs.AIresearchTextual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
arXiv:2609.35285v1 Announce Type: new Abstract: Foundation model recommender systems require user context that can be consumed by large language models, reasoned over, and refined through natural-language interaction. Tr…
- arXiv cs.AIresearchThe Argument and the Letterhead: Source-Position Coherence in AI Evaluation
arXiv:2609.35286v1 Announce Type: new Abstract: An argument can be surprising coming from a particular speaker without being a bad argument. Do AI evaluators keep these judgments apart? Two preregistered descriptive stud…
- arXiv cs.AIresearchAbGaze: Attentive Geometric Representation Learning for End-to-End Antibody Design
arXiv:2609.35296v1 Announce Type: new Abstract: Computational antibody design requires representations that capture the geometric patterns underlying antigen--antibody interactions, yet existing approaches often rely on…
- arXiv cs.AIresearchTraining-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
arXiv:2609.35298v1 Announce Type: new Abstract: Most clinical prediction systems learn patient-variable-outcome associations; we investigate a training-free diagnostic paradigm mapping patient observations to explicit me…
- arXiv cs.AIresearchNarrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculture
arXiv:2609.35302v1 Announce Type: new Abstract: As Large Language Models (LLMs) become central to how we access and share information, they play an increasingly powerful role in shaping global knowledge. However, as thes…
- arXiv cs.AIresearchJev thinks "I don't know'', but doesn't say it: Introducing Sys1Cal-v1 Dataset for Probability Calibration
arXiv:2609.35342v1 Announce Type: new Abstract: The appearance of Jev marked the era of System One Models, foundation models that return structured decisions with probability distributions rather than text. Aside from lo…
- arXiv cs.AIresearchJailbreaks for Black-Box Uncertainty Quantification in Large Reasoning Models
arXiv:2609.35350v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) excel at complex reasoning, alignment through reinforcement learning often induces systemic overconfidence. In production environments,…
- arXiv cs.AIresearchDon't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits
arXiv:2609.35356v3 Announce Type: replace Abstract: Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted generalisation by reques…
- arXiv cs.AIresearchA decision-support system applied to Law: Reasoning and explainability of the decision
arXiv:2609.35370v1 Announce Type: new Abstract: The emergence of the digital transition brought an increasing need to control the processing of digital information, including in Law Enforcement Agencies (LEAs). At the EU…
- arXiv cs.AIresearchStructural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
arXiv:2609.35400v1 Announce Type: new Abstract: Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe…
- arXiv cs.AIresearchBuilding Transformation Layers for Riemannian Neural Networks
arXiv:2609.35436v2 Announce Type: replace Abstract: Recently, deep neural networks on manifold-valued representations have garnered significant attention across various machine learning applications. One recent focus is…
- arXiv cs.AIresearchJust Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization
arXiv:2609.35443v3 Announce Type: replace Abstract: Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primarily improve the optimiz…
- arXiv cs.AIresearchWhy Deterministic PRM Guidance Underperforms in Discrete Diffusion Reasoning
arXiv:2609.35472v1 Announce Type: new Abstract: Discrete diffusion language models (dLLMs) expose a denoised solution at every step, which makes process reward model (PRM) guidance look like a way to spend compute at tes…
- arXiv cs.AIresearchRareDx: Controlled Knowledge Integration and Graph-Grounded Policy Optimization for Rare-Disease Diagnosis
arXiv:2609.35549v2 Announce Type: replace Abstract: Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evidence is distributed a…
- arXiv cs.AIresearchRepresentation Alignment as a Bottleneck in LLM-Based Retrosynthesis Planning
arXiv:2609.35571v1 Announce Type: new Abstract: While LLMs show promise in general reasoning, symbolic planning in chemistry remains a bottleneck. Direct ''SMILES-to-PDDL'' attempts fail because they force models to jugg…
- arXiv cs.AIresearchIMC-CLINIC: Coupled Loss-Informed Newton Iterations for Clipping in Analog In-Memory Computing
arXiv:2609.35586v1 Announce Type: new Abstract: Analog in-memory computing (IMC) offers a promising path toward energy-efficient large language model (LLM) inference by executing matrix multiplications (MatMul) directly…
- arXiv cs.AIresearchSource-preserving alignment for robust evidence localization in scientific PDFS
arXiv:2609.35588v1 Announce Type: new Abstract: Scientific information-extraction systems often return a claim with an evidence string, which users must locate in the original PDF. This is challenging because the extract…
- arXiv cs.AIresearchSignatures of semantic search in the activations of large language models
arXiv:2609.35599v2 Announce Type: replace Abstract: When recalling lists of concepts (e.g., animals) during the semantic fluency task (SFT), both humans and large language models (LLMs) organise their output into cluster…
- arXiv cs.AIresearchTCSAlgBench: Benchmarking Automated Proving for Research-Level Theoretical Computer Science
arXiv:2609.35606v1 Announce Type: new Abstract: Large language models perform strongly on competition mathematics, but their research-level reasoning remains difficult to evaluate systematically. Theoretical computer sci…
- arXiv cs.AIresearchFrom cacophony to hierarchy: a principled framework for assessing AI consciousness
arXiv:2609.35618v2 Announce Type: replace Abstract: The question of AI consciousness is one of the most urgent pre-emptive problems in philosophy and computer science, yet progress is hampered by a cacophony of competing…
- arXiv cs.AIresearchRIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping
arXiv:2609.35623v1 Announce Type: new Abstract: Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups and similar 3D shape wi…
- arXiv cs.AIresearchVerifiable Visual Rewards Transfer from Synthetic Scenes to Natural Prompts
arXiv:2609.35641v1 Announce Type: new Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part because it is learned…
- arXiv cs.AIresearchNot All Thinking is Created Equal: Latent Reasoning Discovers a Recurrent Search Algorithm for Depth Generalization
arXiv:2609.35643v1 Announce Type: new Abstract: Large Language Models can perform multi-step reasoning and improve task performance through different forms of intermediate computation, from token-based traces to computat…
- arXiv cs.AIresearchVerifier Errors in RLVR: Reward Hacking, Limits of Feedback, and Selective Control
arXiv:2609.35677v1 Announce Type: new Abstract: In reinforcement learning with verifiable rewards (RLVR), imperfect verifiers can reward incorrect responses, creating opportunities for reward hacking. Using gradient flow…
- arXiv cs.AIresearchReasoning with Continuous Latent Diffusion
arXiv:2609.35694v3 Announce Type: replace Abstract: We introduce the Continuous Embedding Diffusion Reasoner (CEDR), a training and inference pipeline for continuous latent diffusion models that achieves strong performan…
- arXiv cs.AIresearch$l_{1-2}$ GLasso: $L_{1-2}$ Regularized Multi-task Graphical Lasso for Joint Estimation of eQTL Mapping and Gene Network
arXiv:2301.02225v1 Announce Type: cross Abstract: A critical problem in genetics is to discover how gene expression is regulated within cells. Two major tasks of regulatory association learning are : (i) identifying SNP-…
- arXiv cs.AIresearchSciFlow-Bench: Evaluating Structure-Aware Scientific Diagram Generation via Inverse Parsing
arXiv:2602.09809v3 Announce Type: cross Abstract: Scientific diagrams convey explicit structural information, yet modern text-to-image models often produce visually plausible but structurally incorrect results. Existing…
- arXiv cs.AIresearchChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports
arXiv:2609.31629v1 Announce Type: cross Abstract: Structured phenotype extraction from radiology reports supports cohort construction, quality auditing, and clinical analytics, but practical deployment requires local inf…
- arXiv cs.AIresearchWhat Next-Event Accuracy Cannot See: Closed-Loop Evaluation of Emergency Department Trajectory Simulators
arXiv:2609.31635v1 Announce Type: cross Abstract: Clinical trajectory models are usually evaluated by next-event accuracy on observed histories. Simulation is different: models must condition on their own generated event…
- arXiv cs.AIresearchEnergy-aware frugal Bayesian optimization
arXiv:2609.31638v1 Announce Type: cross Abstract: Modern design optimization frameworks aim first and foremost for models with the most accurate predictions without balancing computational overhead. It remains a reason w…
- arXiv cs.AIresearchWhen Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models
arXiv:2609.31639v1 Announce Type: cross Abstract: Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under operating-condition…