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FormalTCS benchmark evaluates LLMs on frontier theoretical computer science research with 175 expert-validated instances from STOC/FOCS/SODA/COLT 2025–2026.
Sparse Autoencoders reveal feature evolution and migration dynamics across Vision Transformer depth and training time via CLS-token representation analysis.
Hybrid multi-agent framework orchestrates LLM reasoning with RL/rule-based control for autonomous driving to reduce latency and hallucination risk.
We study inference-time control for text generation in discrete diffusion language models, where the goal is to steer sampling toward sequence-level rewards without retraining. Prior work in this domain has focused on particle-based methods such as best-of-$n$ sampling and bootstrap sequential Monte Carlo, which may suffer from overoptimism and weight degeneracy, respectively. We address these limitations using \emph{nested} sequential Monte Carlo methods. We formulate nested SMC (NSMC) and fully-adapted nested SMC (FA-NSMC) for Feynman--Kac steering, identifying and correcting errors in prio...
The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning. However, their adoption in operational settings remains limited by the lack of models' interpretability. While for conventional text and image modalities, tools such as Sparse Autoencoders (SAEs) have proven effective for extracting human-understandable concepts, their use for the analysis of ExEE remains challenging due to the nature of W&C data. To address this, we introduce (i) a geographic location-based modulation of the ...
Large language models (LLMs) are increasingly used in settings where textual summaries, numerical observations, and external tool outputs may provide conflicting evidence. We study how LLMs arbitrate between such sources when they support opposing decisions. To do so, we introduce a controlled synthetic benchmark in which latent risk trajectories generate both numerical time series and natural language summaries, allowing us to construct conflicts where exactly one evidence source is aligned with the ground-truth label. This design lets us independently manipulate modality, temporal recency, ...
Mobile manipulation requires a robot to predict how locomotion and arm motion jointly alter future observations and control. Existing world-action models, developed largely for fixed-base platforms, do not explicitly distinguish camera ego-motion from base and arm actions. Here we introduce DECOWAM, a whole-body world-action model that separates these factors through dedicated conditional interfaces. DECOWAM freezes an adapted FastWAM backbone and trains residual adapters, an action-equivalent future bottleneck distilled from privileged observations, adversarially separated base and arm laten...
We introduce OenoBench, a wine-domain knowledge benchmark of 3,266 multiple-choice questions across six pillars (regions, grape varieties, viticulture, winemaking, producers, business) and four difficulty tiers. The corpus is built from 38,104 atomic, source-anchored facts extracted by 35 provenance-verified scrapers from government registries (INAO, TTB, OIV), peer-reviewed journals, and Wikipedia/Wikidata. Our methodological contribution is an LLM-driven pipeline in which language models reformat verified facts and audit the result, but never serve as the source of truth: every claim traces...
Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinity that ignores spatial structure. This paper studies whether structured, gradient-free immune affinity can make Deep AINs viable as replay-free visual class-incremental representation-memory learners. Visual B-cells are formalized as structured templates, including shifted-template affinity, zero-normalized cross-correlation (ZNCC) filters, and feature-map binding profiles. A repertoire is treated both as memory and as a representation-inducing basis, whil...
LLM-based Multi-Agent Systems (MAS) achieve strong performance on complex reasoning tasks by coordinating multiple agents, but at the cost of substantial token consumption. Recent work on automatic topology design, ARG-Designer, has reframed this problem as autoregressive graph generation. However, its training objective provides no explicit incentive for the model to generate sparse and efficient topologies. We address this limitation by introducing a Reward-Guided Autoregressive Graph Generation (RGA-Designer) inspired by Reinforcement Learning from Human Feedback (RLHF). We train a reward ...
AdaPT framework trains humanoid robots to perform professional tennis serves and rallies by learning motion styles from broadcast videos using hierarchical planning and tracking.
EAFG framework combines Vision-Language Models with Task and Motion Planning to verify object availability before generating subgoals in long-horizon robotic manipulation.
SABET-QA uses iterative refinement with bidirectional entity-temporal scoring to answer multi-hop questions over temporal knowledge graphs.
Today on Decoder, I’m talking with Robert Hart, The Verge’s London-based AI reporter, about what AI is doing to the field of mathematics and the existential crisis many lead mathematicians are having about it. OpenAI just published a set of solutions to longstanding problems in math that went off like a bombshell in the field. It caused a huge debate in the math community, and Rob spent some time talking to some of the most accomplished mathematicians of our time about it. It’s funny that AI systems are all still pretty bad at elementary school arithmetic, but getting increasingly good at ver...
Orthogonal JEPA factorizes world model predictive states into independent pathways to reduce capacity waste and improve gradient signals for complex systems.
Two-step hyperparameter transfer framework enables compute-efficient learning rate optimization for trillion-token Mixture-of-Experts models via scaling extrapolation.
EchoCoT systematically extracts hidden chain-of-thought traces from proprietary reasoning models via API interaction using fidelity signals as recovery feedback.
Restricting evidence visibility across multi-module language-model societies improves compositional generalization compared to full-input exposure in relay-based architectures.
Safety Nets framework combines neural network compression with lookup tables to certify 100% correct behavior for AI systems in EASA aviation applications.
DecoVAE decomposes time series into trend and seasonal components using domain-specific inductive biases for lightweight, interpretable probabilistic forecasting.
Empirical evaluation framework audits cross-lingual fairness in LLM watermarking with calibrated thresholds and threshold-independent quality metrics across languages.
Cryptographic Context Injection is only the latest way to break an LLM safety guardrail.
Meta said its Muse Spark model is powering the dictation feature.
Mistral launches agentic search layer for document retrieval and verification in AI systems.
Slack is introducing dedicated channels where teams can vibe code together with AI agents instead of jumping between different tools and conversations. The Slack Code launch includes open, project-specific code channels with dedicated user tabs, alongside features that compare coding changes and preview HTML output before the project is shipped. "With Slack Code, when you have an idea or need to build a new feature, update a web page, or fix a bug, you simply tag in a coding agent like Anthropic's Claude or Cognition's Devin, and that agent then spins up a code channel to tackle the task," Sl...
Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name…
Binance's Agent OS works with tools including ChatGPT, Claude Code, and Cursor.
OpenAI launches Intelligence Age blog exploring AI's impact on power, governance, economy, and freedom.
Z.ai CEO Jie Tang discusses GLM 5.3 model and post-training scaling laws as alternative to parameter growth.
Stampli compressed product launch timeline using ChatGPT Work and Codex for development acceleration.