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Wszystko, co pojawiło się w monitorowanych źródłach – od najnowszych.
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Beyond Answer Changes: Matched Same-Class Controls for Local Audio Evidence
[cs.SD] Autorzy: Yunuo Wang, Maoyuan Chen, Yuanjin Huang i 1 więcej For audio-language models, an answer change after a local sound-event edit can reflect a response to the event's class, sensitivity to waveform rewriting, or both. We compare matched same-class and different-class replacements of a queried event. Both use the same interval, level estimate, donor fitting, and crossfade, while the same-class replacement preserves the correct answer. Across 600 synthetic soundscapes and three models from two architecture families, different-class replacements produce 49.7 to 60.9 percentage points more answer changes than same-class replacements. Same-class accuracy remains within 5.2 percentage points of original accuracy. Among trials with correct original and same-class answers, soundscape-weighted selective accuracy for the inserted class is 73.7 to 81.9%, averaging eligible trials within each soundscape and then weighting eligible soundscapes equally.…
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MoI-Bench: A Benchmark for Image Generation and Editing on Mobile Devices
[Widzenie komputerowe] Autorzy: Kailai Feng, Haoxiang Rao, Yuxiang Wei i 5 więcej Developing on-device image models requires considering practical user needs, task types and device resource constraints. However, existing benchmarks provide limited guidance on identifying capability gaps between on-device models and server-side models. We introduce MoI-Bench, a benchmark grounded in mobile user needs, comprising 64 subtasks and 1150 samples. It defines 16 operations, such as removal and replacement, and eight visual target categories covering content such as portraits and text. These form two orthogonal axes whose applicable combinations define specific tasks, such as removing facial makeup. To better guide model improvement, we further propose MoI-Eval, which introduces task-specific criteria covering instruction following, visual quality and detail preservation. We further construct sample-level evaluation questions and aggregate their scores into overall quality an…
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GAS-PFN: Graph-Aware Spatiotemporal Prior-Data Fitted Networks for Zero-Shot Traffic Forecasting
[Uczenie maszynowe] Autorzy: Joseph Natter, Long Vu, Yifan Zhang i 3 więcej Traffic forecasting requires modeling complex temporal dynamics and the spatial dependencies imposed by road topology. Supervised graph-based models achieve strong performance, but are often trained for a particular dataset and sensor graph, making deployment to new or evolving networks costly. Prior-Data Fitted Networks (PFNs) offer a zero-shot alternative by learning in-context inference from synthetic task priors, which provide an explicit mechanism for encoding domain-specific assumptions. Existing forecasting PFNs, however, do not explicitly represent the graph-structured dependencies governing traffic dynamics. To combine graph-aware traffic modeling with zero-shot adaptation, we introduce the Graph-Aware Spatiotemporal Prior-Data Fitted Network (GAS-PFN). GAS-PFN incorporates sampled road topology into its synthetic task prior through random-walk diffusion, teaching the model to …
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Understanding the limits of latent world models
[Uczenie maszynowe] Autorzy: Rodrigo Gonzalez Laiz, Steffen Schneider World models are widely proposed as core components for enabling agents to reason over long temporal and spatial horizons. Yet current evaluations often emphasize task performance while providing limited insight into whether learned world models acquire predictive latent dynamics that reflect the underlying structure of the environment. Here we introduce the concept of faithful latent world models as an equivariance condition between the environment's transition map and the learned latent dynamics. We propose latent rollout consistency (LRC) as a metric to evaluate action-conditioned latent prediction. Strikingly, we find that across the studied state of the art latent world models, none solves the task by actually faithfully modeling the dynamics of the environment.
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Timestral
[Sztuczna inteligencja] Autorzy: Adrien Petralia, Alexandre Gavaudan, Artjom Joosen i 353 więcej We introduce Timestral, a family of four time series foundation models for probabilistic forecasting, with 5M, 28M, 123M, and 209M parameters. Timestral sets a new state of the art on GIFT-Eval, TIME, and fev-bench, where Timestral-209M and Timestral-123M take the top two places on every metric. We achieve this with three ingredients: Decoupled Horizon Forecasting (DHF), which separates prediction tokens from the causally encoded context, so that longer horizons are trained without masking context and each observed value is both a forecast target and context for later forecasts; a covariate framework that natively supports known-future covariates within a causal architecture; and a two-stage pipeline that pretrains exclusively on a diverse, large-scale synthetic corpus before post-training on a mix of real and synthetic data with extended context. This report describes the model design …
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REGER: Modeling inter-item Relations with Semantic IDs for Generative Recommendation
[cs.IR] Autorzy: Yuxi Huang, Silin Zhou, Chenhao Wang i 5 więcej Personalized recommendation depends on learning semantic and collaborative relations among items from online content and user interactions. Generative recommendation (GR) offers a promising paradigm by autoregressively generating discrete semantic IDs (SIDs) of items. However, randomly initialized SID token embeddings and GR models trained from scratch do not directly inherit the semantic relations inherent in items, while inter-item collaborative relations are learned largely implicitly through token-level attention and next-token supervision. To address this limitation, we propose REGER, a unified framework that explicitly incorporates inter-item relations into semantic pretraining, history encoding, and SID generation. We pretrain the GR model through generative semantic retrieval, enabling it to inherit inter-item semantic similarity by learning to generate semantic neighbors' SIDs.…
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TeamGym: A Gym for Team Decision-Making by AI Agents with Distributed Information
[Sztuczna inteligencja] Autorzy: Chanwoo Park, Jaywoong Jeong, Zachary Wojtowicz i 4 więcej Many real-world decisions require teams to integrate information distributed across participants. Yet AI agents are still primarily trained and benchmarked in isolation. We introduce TeamGym, an environment for evaluating and training language model-based agents on team decision-making problems constructed from real-world legal, finance, medical, and software engineering corpora. TeamGym introduces a method to identify a minimal set of facts, each element of which is necessary and all of which are collectively sufficient for a correct final decision, which we call pivotal information. This enables TeamGym to both control the distribution of pivotal information and track how well agents identify and integrate pivotal information when making team decisions. Examining 8 language models on 12 source corpora, we find that current AI agents often fail to effectively share information: Distrib…
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Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents
[Uczenie maszynowe] Autorzy: Pranjal Aggarwal, Sean Welleck AI agents have made substantial progress in domains where they have a non-trivial amount of time to deliberate between actions, such as mathematics, software engineering, and performing tasks on desktop applications. It is less clear how such models fare in settings that demand real-time interaction, such as playing a video game or operating a physical device. In these settings, the environment state may evolve as the agent is reasoning, or even as the underlying model is performing a forward pass. To gain insight into the real-time interaction capabilities of vision-language agents, we introduce Gym-Anything-RT. Our key idea is to construct visual minigames that demand a portfolio of capabilities required for interactive real-time settings. Gym-Anything-RT uses an agentic pipeline to automatically generate such environments based on a taxonomy of six capabilities required for real-time…
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When uncertainty quantification fails: structural vulnerabilities and corrections for selective prediction under clinical class imbalance
[Uczenie maszynowe] Autorzy: Walter Endrizzi, Flavio Ragni, Raffaele Marchesi i 4 więcej Background. Selective prediction systems improve the safety of clinical artificial intelligence by deferring ambiguous cases to human experts while automating high-confidence decisions. However, this safety mechanism relies entirely on the premise that uncertainty scores accurately reflect clinical ambiguity. In prognostic tasks characterized by severe class imbalance, where the clinical outcome of interest is a minority, this assumption breaks down. Standard uncertainty measures cease to capture clinically aligned ambiguity; instead, they become a proxy for class membership, systematically penalizing minority patients regardless of their individual predictive clarity. Results. Across 56 synthetic conditions and three neurodegenerative cohorts spanning severe class imbalance to near-balanced conditions (multiple sclerosis, Parkinson's Disease, Alzheimer's Disease), our framework consist…
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Introducing LFM3: Every Modality for Specialized Intelligence
164 punktów · 62 komentarze · autor Philpax · www.liquid.ai
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Kinematic Constraint-Referenced Evaluation for Articulated Object Manipulation
[Robotyka] Autorzy: Baptiste Bonin, Caro Strickland, Audrey Durand Manipulation policies are evaluated predominantly by binary task success, which cannot tell whether the commanded gesture is the one the object's kinematics admit. We introduce Constraint-Referenced Strategy (CRS), which projects the commanded end-effector twist onto the object's joint axis, separating progress along the joint from off-constraint waste. CRS requires a joint model and a joint-following scripted controller derived from it, but no recorded demonstration or learned judge. On 74 simulated articulated objects, success registers three transfer failures as lower rates but attributes none, while the two CRS channels give each a distinct signature. A door-trained behavior-cloning policy opens drawers in 59 of 110 episodes, and in these passes it commands about $40\times$ the off-axis rotation of a drawer-trained policy's passes ($1.27$ vs.\ $0.032$~rad/s). Relative to a scripted …
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When, Where, and What to Correct: Human-Guided Adaptive Residual Correction for Robot Policies
[Robotyka] Autorzy: Anukriti Singh, Maxime Caniot, Pratap Tokekar i 1 więcej Imitation policies that reliably execute most of a manipulation trajectory can still fail in localized regions requiring last-mile precision. Updating the full policy or exploring residual actions everywhere treats this local failure as a global learning problem. We introduce Adaptive Residual Correction (ARC), a lightweight post-training module that uses reinforcement learning (RL) to learn a residual correction on top of a frozen policy while using sparse human corrections to structure autonomous exploration. Rather than treating human corrections as imitation targets, ARC uses pre-intervention states to determine 'when' residual control should become active and correction geometry to determine 'where' in residual action space autonomous refinement should explore; task reward determines 'what' residual is ultimately executed. Across three precision-manipulation tasks on a real robot, …
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GroundControl: Satellite-Conditioned Multi-View Diffusion for Reconstruction-Free Novel View Synthesis
[Widzenie komputerowe] Autorzy: Akshay Krishnan, Matias Turkulainen, Jamie Wynn i 7 więcej Multi-view or video diffusion models have recently emerged as state-of-the-art approaches for novel-view synthesis from sparse image collections. While they can produce high quality images, they tend to hallucinate unrealistic spatial layouts when synthesizing views outside the input views (scene extrapolation). We propose to address this challenge for outdoor scenes by using satellite images as an additional information source to condition the multi-view generative model. We introduce a new satellite-conditioned multi-view diffusion model that combines conditioning from a satellite image and street-level input images when generating novel-views. Our cross-attention based conditioning allows us to retain pretrained multi-view diffusion priors to generate seamless, realistic, novel views from a handful of source views, while also adhering to the scene layout as seen from satellite images…
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Which Hand Was Late? Diagnosis for Robot Failure Recovery in Bimanual Tool Use
[Robotyka] Autorzy: Baptiste Bonin, Caro Strickland, Audrey Durand Failure diagnosers for robot manipulation are increasingly vision--language models (VLMs), trained and evaluated on single-arm failures produced by perturbing a successful demonstration. Bimanual tool use adds failures of other kinds: a tool that slips out of its socket while still held, two correct gestures made at different times, and attempts that fail for two reasons at once. On a simulated bimanual tool-use task whose failure causes are read from the recorded trace, we measure what each sensing channel lets a diagnoser tell. The tool's slip is read on the tool hand; which hand was late is read on both arms, while a sensor on one hand reads its own hand's delay and at most part of the other's; and whether a late attempt was also pushed too weakly is not identifiable from passive sensing when the tool hand is late. Within two attempts, a repair policy that escalates when the cause is…
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Ghostty 1.4
1 275 punktów · 407 komentarzy · autor deaux · ghostty.org
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Police are using AI to pull over drivers for 'suspicious' behavior
390 punktów · 305 komentarzy · autor gumby · gizmodo.com
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A 28-Year-Old DOGE Operative Gutted USAID. HR Had No Record of Him Working There
240 punktów · 166 komentarzy · autor ck2 · www.wired.com
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Classification of Finite Simple Groups Formalized in Lean
236 punktów · 74 komentarze · autor pfdietz · cfsg.axiommath.ai
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Amazon Is Gutting Its AI Division After Sustained Failure
360 punktów · 276 komentarzy · autor dpflan · futurism.com
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New 14‑inch MacBook Pro with M6
403 punktów · 556 komentarzy · autor beex · www.apple.com
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Magnituda 4,9 — 70 km WNW of Callaguip, Philippines
Głębokość 10 km.
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Genie 4
986 punktów · 562 komentarze · autor acossta · deepmind.google
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Magnituda 4,8 — south of the Fiji Islands
Głębokość 497 km.
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Anthropic trained Claude on illegally obtained Stanford professors' books
550 punktów · 282 komentarze · autor car · stanforddaily.com
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Magnituda 5,0 — 29 km W of Río Grande, Panama
Głębokość 10 km. Zgłoszeń od świadków: 2.
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A $300B 'Debt Wall' Is Looming over Private-Equity Backed Companies
171 punktów · 62 komentarze · autor JumpCrisscross · www.wsj.com
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Magnituda 5,4 — 126 km S of Pundong, Indonesia
Głębokość 10 km. Zgłoszeń od świadków: 3.
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Magnituda 4,9 — 156 km S of Pundong, Indonesia
Głębokość 21 km.
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Magnituda 4,6 — 41 km S of Farkhār, Afghanistan
Głębokość 115 km.
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US Government is hiding a secret court ruling about how it spies on Americans
1 035 punktów · 412 komentarzy · autor helterskelter2 · www.wired.com