3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2608.07079v2 Announce Type: replace-cross Abstract: Object-goal navigation has made substantial progress in semantic perception and exploration, yet persistent memory for multi-object navigation and cross-floor nav…
arXiv:2608.07208v2 Announce Type: replace-cross Abstract: Recent work shows that Large Language Models (LLMs) encode more information in their activations than they show in their responses.
arXiv:2608.11242v2 Announce Type: replace-cross Abstract: When the context window is under pressure, LLM systems compact prior context to continue ongoing tasks.
arXiv:2608.11947v2 Announce Type: replace-cross Abstract: Multiple-choice question (MCQ) benchmarks are widely used to evaluate large language models, yet a single reported accuracy number is not enough to distinguish a…
arXiv:2608.14094v2 Announce Type: replace-cross Abstract: Cloud-local LLM inference can use the reasoning capability of large cloud models, but cloud-bound requests must exclude personally identifiable information (PII).
arXiv:2608.14606v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations mostly ask whether individual answers look plausible.
arXiv:2608.18779v2 Announce Type: replace-cross Abstract: Semantic ID tokenizers assign code sequences to catalog items, which generative recommenders predict and resolve back to items.
arXiv:2608.28837v2 Announce Type: replace-cross Abstract: We conducted an interview study with twelve students on their use of generative AI in academic communication.
arXiv:2609.01507v3 Announce Type: replace-cross Abstract: Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model.
arXiv:2609.02085v2 Announce Type: replace-cross Abstract: We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at $6-24$h leads by leveraging a fo…
arXiv:2609.05742v2 Announce Type: replace-cross Abstract: American Sign Language (ASL) generation remains challenging due to limited paired text-ASL motion data and the difficulty of learning motion representations both…
arXiv:2609.07629v2 Announce Type: replace-cross Abstract: Democratizing access to the knowledge held in large corpora of tables such as data lakes is emerging as a central research challenge.
arXiv:2609.08339v2 Announce Type: replace-cross Abstract: Bimanual manipulation policies require large and diverse training datasets, yet collecting demonstrations on physical robots is expensive and difficult to scale.
arXiv:2609.09090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) may abandon correct positions when users push back, exhibiting a failure mode known as sycophancy.
arXiv:2609.15383v2 Announce Type: replace-cross Abstract: Language model safety is typically evaluated one interaction at a time.
Graduates are increasingly using AI to misrepresent themselves in job applications, research suggests.
Michael I.
Character.ai has filed for bankruptcy and is looking for a buyer.
The full TechCrunch Disrupt Stage lineup revealed, featuring Max Hodak, Mark Wahlberg, Benchmark partners, and more.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
(Soft prerequisites: A high-level model of AI bargaining and Taboo “equilibrium”: Less confused frames for research on AI bargaining .) Section II.1 of CLR’s safe Pareto improvements (SPI) agenda briefly reviews the chal…
Molmo 2’s open release helped MIT researcher Kai Van Brunt develop Molmo2Fish, an experimental model that tracks salmon in sonar footage and corrects some tracking errors using everyday language.
Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response reuse.
A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions.
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents.
Reconstructing complete, high-quality meshes of large-scale scenes from unposed multi-view images requires faithful geometric grounding while plausibly completing surfaces that are unobserved or only sparsely captured.
Large language models have evolved from single-turn chatbots into interactive agents capable of long-horizon reasoning.
Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements.
Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence.
Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental consequences.
We introduce Fish Audio Drama 3, an open-source speech generation model that turns text into directed performances, built on a 35B-parameter mixture-of-experts backbone that activates 3B parameters per token.
Visual agents must turn video observations into timely decisions to track progress and choose what to do next.
Rubric-based reward models (RMs) evaluate responses against explicit criteria.
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold.
Good empirical safety research is heavily bottlenecked on thinking more clearly.
In collaboration with scientists and research partners, Google is helping to make disease more detectable, treatable, and preventable — with the goal of improving health…
Pew Research Center finds 51% of Americans say no, cite lack of interest - not know-how - as the top reason
Research: Profiling web-edit with Python 3.15: flame graphs and source heatmaps I put the new Python 3.15 profiler through its paces by having GPT-6 Astra Extra High in Codex run it against my new web-edit app : I need a…
Epistemic status: I traced one famous anecdote to its printed source and to both coauthors' own accounts of where it came from.
In an earlier post I showed that when AI systems can perform a task, they do it much more cheaply than a human worker.
This White Paper provides roboticists and control engineers with a comprehensive overview of how motion planning and motion tracking can be decoupled to preserve professional athletic style in humanoid robots, and why ad…
Google Deepmind's AlphaProof Nexus solved dozens of open math problems, including nine Erdős problems two of which were unsolved for 56 years, per a study in Science.
Ambitious mechanistic interpretability research is full of jargon, but the underlying intuitions are so simple that a five-year-old (with a firm grasp of linear algebra) could understand them.
arXiv:2610.13212v2 Announce Type: replace-cross Abstract: Actions specify what a robot should execute, but do not describe what those commands will achieve.
arXiv:2610.13935v2 Announce Type: replace-cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models.
arXiv:2610.12914v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong promise in robotic manipulation, yet their post-training still predominantly relies on trajectory-level supervised imi…
arXiv:2610.12927v1 Announce Type: new Abstract: Large language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context reasoning, and test-time computation.
arXiv:2610.13112v1 Announce Type: new Abstract: Large audio-language models (LALMs) frequently exhibit object hallucination, affirming absent sounds.
arXiv:2610.13131v1 Announce Type: new Abstract: Trajectory generation underpins urban applications such as traffic management and urban planning.
arXiv:2610.13285v1 Announce Type: new Abstract: Distilling long-chain reasoning trajectories from large teacher models provides an effective way to improve the reasoning ability of small language models.