AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMHWA: Multi-timescale Hierarchical World-Action Model
arXiv:2610.14727v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increasing drift that leads…
- arXiv cs.AIresearchSAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability
arXiv:2610.14745v1 Announce Type: new Abstract: Extreme low-bit weight-only quantization is an effective approach to reducing the deployment costs of large language models. Below 3 bits, scalar quantization often suffers…
- arXiv cs.AIresearchEqual-information evaluation of credit assignment in modular financial reinforcement learning
arXiv:2610.14750v1 Announce Type: new Abstract: Credit assignment in modular financial reinforcement learning is difficult to evaluate because an attributed signal may improve a component loss without adding decision-rel…
- arXiv cs.AIresearchGateSight: Recurrent Memory Anticipates Attention Memory for Query-Agnostic KV Eviction in Hybrid LLMs
arXiv:2610.14754v1 Announce Type: new Abstract: Hybrid language models interleave Gated DeltaNet (GDN) layers, whose recurrent memory has a fixed size, with full-attention layers, whose key-value (KV) cache grows with th…
- arXiv cs.AIresearchCUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation
arXiv:2610.14763v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target infor…
- arXiv cs.AIresearchLYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement
arXiv:2610.14777v1 Announce Type: new Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing approaches, whether t…
- arXiv cs.AIresearchGREAT-ESDD: Graph Edge Attention Network for Effectively Steered Delivery Decisions
arXiv:2610.14792v1 Announce Type: new Abstract: Online, same-day delivery requires operators to constantly update their routes, and the routing cost for deliveries increases with the number of customers. Out-of-home (OOH…
- arXiv cs.AIresearchCESNET-TimeSeries26: A Multi-Year Network Traffic Time Series Dataset for Annual Seasonality, Concept Drift, and Long-Term Anomaly Detection
arXiv:2610.14799v1 Announce Type: new Abstract: Forecast-based models are fundamental to network traffic management and anomaly detection. However, it is essential to evaluate these forecasting models over extended perio…
- arXiv cs.AIresearchPre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime
arXiv:2610.14843v1 Announce Type: new Abstract: Claims of recursive self-improvement are easy to make and hard to falsify: did improvement come from re-using designer-provided mechanisms, or from mechanisms the system au…
- arXiv cs.AIresearchWeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models
arXiv:2610.14863v1 Announce Type: new Abstract: Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numerical weather predicti…
- arXiv cs.AIresearchProbable but Not Valid: A Psychometric Audit of Decision Models as Synthetic Survey Respondents
arXiv:2610.14912v1 Announce Type: new Abstract: Language models are increasingly used to create synthetic survey samples, also called silicon samples. Decision models are a new class of language models that return a prob…
- arXiv cs.AIresearchBridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling
arXiv:2610.14913v1 Announce Type: new Abstract: Reinforcement learning (RL) is essential for adaptive decision-making, e.g., robust Meta-RL and domain randomization (DR), and efficient post-training of large language mod…
- arXiv cs.AIresearchPredicting how model propensities generalize out of distribution
arXiv:2610.14937v1 Announce Type: new Abstract: How do language models generalize from their training data? This is a central challenge for alignment: models reliably learn their training distribution given enough sample…
- arXiv cs.AIresearchVC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning
arXiv:2610.14939v1 Announce Type: new Abstract: GRPO-style reinforcement learning has advanced vision-language reasoning, but its sequence-level rewards provide limited credit assignment for individual reasoning tokens.…
- arXiv cs.AIresearchAlphaRLM: Aligning Language Models for Literature-Grounded Symbolic Factor Discovery
arXiv:2610.14959v3 Announce Type: replace Abstract: Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space. Recent LLM-based approaches use pr…
- arXiv cs.AIresearchFleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization
arXiv:2610.14970v1 Announce Type: new Abstract: With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model behavior to individual…
- arXiv cs.AIresearchSingle-Sample Decoding of a Two-Layer Sound-Word Canvas Model for Thai Klon Pad
arXiv:2610.14980v1 Announce Type: new Abstract: We present a generator of Thai klon pad verse, one stanza from a short topic, and evaluate it against human verse on held-out topics. The generator is a masked language mod…
- arXiv cs.AIresearchCan You Trust Your Preference Models? Stackelberg Games for Alignment
arXiv:2610.15008v1 Announce Type: new Abstract: Language models are frequently aligned to human preferences via imperfect evaluators whose errors can be exploited during optimization. We train policies to search for comp…
- arXiv cs.AIresearchOn the Complexity Gap Between POMDPs and Multi-Environment POMDPs
arXiv:2610.15010v1 Announce Type: new Abstract: Multi-environment POMDPs (ME-POMDPs) generalize POMDPs by encoding a finite set of possible environments, capturing model uncertainty. The finite-horizon robust reward maxi…
- arXiv cs.AIresearchExposing AI Inference as a Telco Network API over the 5G User Plane
arXiv:2610.15062v1 Announce Type: new Abstract: Supporting edge Artificial Intelligence (AI) inference as a network service is challenging because current mobile systems can transport tensors but do not expose or underst…
- arXiv cs.AIresearchKnowledge transfer between simple discrete worlds
arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying general intelligence. We…
- arXiv cs.AIresearchStructural Chain-of-Thought: Reasoning with Executable Computational Graphs
arXiv:2610.15107v1 Announce Type: new Abstract: Chain-of-thought prompting enables reasoning in large language models but produces explanations that are difficult to verify, expensive to generate, and not always faithful…
- arXiv cs.AIresearchGesBench: When and Where Do Multimodal Language Models Perceive Social Meaning?
arXiv:2610.15129v1 Announce Type: new Abstract: Human social perception is incremental: we infer intent before an utterance is complete, revise our interpretation as new cues arrive, and attend to specific facial, gestur…
- arXiv cs.AIresearchTrust Hinders Bidirectional Human-AI Alignment
arXiv:2610.15136v2 Announce Type: replace Abstract: Trust calibration remains the dominant framing of AI-assisted decision making: the goal is to close the gap between perceived and actual trustworthiness of AI systems s…
- arXiv cs.AIresearchSingle-step Retrosynthesis via Reinforcement Learning from Forward Feedback
arXiv:2610.15141v1 Announce Type: new Abstract: Single-step retrosynthesis is a one-to-many inverse problem, yet most models are trained and evaluated against a single recorded reactant set. Reference-based training give…
- arXiv cs.AIresearchContinuous Risk-Based Scheduling for Food Safety Inspection: Integrating Historical Decay with Event-Driven Consumer Review Signals
arXiv:2610.15191v1 Announce Type: new Abstract: Batch risk-based inspection scheduling struggles to detect a critical failure class: food facilities with recent clean inspections that subsequently develop severe violatio…
- arXiv cs.AIresearchSocraticRepair: Planning-Guided Socratic Tutoring for Program Debugging and Misconception Repair
arXiv:2610.15204v1 Announce Type: new Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited: they tend to reveal t…
- arXiv cs.AIresearchRunning Fewer Loops Removes Refusal Behaviour in Recurrent-Depth Language Models
arXiv:2610.15218v1 Announce Type: new Abstract: A recurrent-depth language model reuses one block of weights several times in a row. The number of repetitions, which we call loops, is chosen when the model runs, not when…
- arXiv cs.AIresearchUnderstanding Unified VLMs: From Attention Probing to Guidance Tuning for Multimodal Generation
arXiv:2610.15227v1 Announce Type: new Abstract: Unified vision-language models (VLMs) perform both text-to-image (T2I) and image-to-text (I2T) generation with one shared Transformer backbone, yet how the backbone process…
- arXiv cs.AIresearchWords to World: Benchmarking LLMs in Interactive Physical Environments
arXiv:2610.15228v1 Announce Type: new Abstract: Words are increasingly becoming an interface for action: large language models (LLMs) can now invoke tools, operate devices, and make decisions that affect the physical wor…
- arXiv cs.AIresearchUsing Evidence-Centered Design to Guide Prompt Engineering in Measurement: An Illustration with Automated Essay Scoring
arXiv:2610.15231v2 Announce Type: replace Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks such as item gener…
- arXiv cs.AIresearchMoMaTune: Model, Match, Then Tune Language Models for Numerical Records
arXiv:2610.15235v1 Announce Type: new Abstract: Despite remarkable progress in language understanding and generation, decoder language models continue to exhibit striking failures on numerical data. Numerical examples ar…
- arXiv cs.AIresearchClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail
arXiv:2610.15239v1 Announce Type: new Abstract: Most benchmarks for large language models in medicine are built from licensing examinations and theoretical questions. The few that draw on real clinical data do so only in…
- arXiv cs.AIresearchRECAP: Calibrating Low-Bit Reasoning Models on the History They Read
arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and values spanning thou…
- arXiv cs.AIresearchCHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition
arXiv:2610.15298v1 Announce Type: new Abstract: Large language models (LLMs) offer a new approach to wearable-sensor-based human activity recognition. Retrieval-augmented generation (RAG) further introduces similar motio…
- arXiv cs.AIresearchSuperAI: Merging Capabilities Across Heterogeneous Foundation Models
arXiv:2610.15322v2 Announce Type: replace Abstract: Enabling the reuse and composition of knowledge and capabilities distributed across foundation models is an important step toward building scalable artificial intellige…
- arXiv cs.AIresearchMultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching
arXiv:2610.15339v1 Announce Type: new Abstract: Current fMRI encoding models frame prediction as deterministic regression, yet identical stimuli evoke a distribution of neural responses (arising from physiological noise,…
- arXiv cs.AIresearchTRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation
arXiv:2610.15365v1 Announce Type: new Abstract: Large language models (LLMs) can produce harmful content while generating a response. A useful safeguard should not only detect this risk during generation but also interve…
- arXiv cs.AIresearchFewer Bits, More Tries: Recovering Capability Under Memory Constraints
arXiv:2610.15372v1 Announce Type: new Abstract: Large language models are generally more capable than smaller ones, but their memory requirements can make local deployment difficult. We ask whether part of this capabilit…
- arXiv cs.AIresearchAnalyzing the Dynamics of WSBM-based Echo State Networks using InputDSA
arXiv:2610.15380v1 Announce Type: new Abstract: Bridging the structural gap between biological brains and artificial neural networks presents a pathway to altered computational dynamics. This paper introduces a Reservoir…
- arXiv cs.AIresearchA Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning
arXiv:2610.15403v1 Announce Type: new Abstract: Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geographic value depends on wh…
- arXiv cs.AIresearchSpecFit: Not All Draft Tokens Cost the Same in Offloaded MoE Inference
arXiv:2610.15417v1 Announce Type: new Abstract: Mixture-of-experts (MoE) models activate only a few experts per token, but when their expert weights are offloaded to host memory, host-to-device (H2D) expert transfers ent…
- arXiv cs.AIresearchHidden in Plain Sight: Advancing Harmful Content Understanding via Perception-Aware Adversarial Self-Play
arXiv:2610.15449v2 Announce Type: replace Abstract: Despite significant advances in language understanding, Large Language Models (LLMs) can still miss meaning hidden in plain sight, failing to identify harmful intent in…
- arXiv cs.AIresearchScalable Robust MCTS with Anytime Deterministic Optimality Guarantees
arXiv:2610.15461v1 Announce Type: new Abstract: MDP planners often rely on nominal transition models that may differ from the true dynamics. Robust MDP planning accounts for this uncertainty through a set of admissible m…
- arXiv cs.AIresearchMemeLogos: Knowledge-Driven Retrieval-Augmented Reasoning for Harmful Meme Detection
arXiv:2610.15463v2 Announce Type: replace Abstract: With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly conveyed through co…
- arXiv cs.AIresearchLarge Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers
arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does not explicitly expos…
- arXiv cs.AIresearchData Portability Revisited: Toward the Human-Centric, AI-Driven Data Ecosystems of Tomorrow
arXiv:2610.15503v1 Announce Type: new Abstract: This Article critically examines the contemporary regulatory framework and discourse surrounding data portability in the United States. Using recent regulatory developments…
- arXiv cs.AIresearchSelf-Representation Alignment in Diffusion Models for Video Background Music Generation
arXiv:2610.15575v1 Announce Type: new Abstract: Diffusion models have been widely used for various generative tasks. However, recent studies have demonstrated that the internal representations learned in the diffusion mo…
- arXiv cs.AIresearchCondMSP: Condition-Informed and Resource-Aware Inorganic Materials Synthesis Planning
arXiv:2610.15603v1 Announce Type: new Abstract: Materials synthesis planning (MSP) connects AI-driven materials discovery to experimental realization by proposing precursors, synthesis operations, and processing conditio…
- arXiv cs.AIresearchFrom Stages to States: Unified Iterative Reasoning for MLLMs
arXiv:2610.15620v2 Announce Type: replace Abstract: Multimodal Chain-of-Thought (MCoT) substantially enhances the complex reasoning capabilities of Multimodal Large Language Models (MLLMs) by explicitly modeling intermed…