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14,558 papers
Time-series diagnostic systems rarely rely on retrieving relevant historical cases, and when they do, retrieval is evaluated only indirectly through downstream prediction. We introduce READ-Bench, a benchmark for historical-case retrieval across 12 diagnostic datasets, centered on multivariate time series, that defines...
A simple barrier model is introduced where each prompt has a baseline safety level and each augmentation a random thermally activated barrier determines the entire ($N, M$) attack surface.
Recurrent state provides a complementary carrier of workspace content, whose recovery, persistence and causal uses require separate measurements, and sign-guarded steering, which improves target top-five success over coordinate exchange on matched verbal-report trials in five models.
GameBoyWorlds is introduced, a testbed for agentic self-improvement in video games and it is shown that contemporary approaches to self-improvement are lacking, with world modelling and autonomous skill discovery failing, and a novel strategy that uses curiosity-based exploration to write guides achieving only partial...
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AI agents are stateless across sessions by default and therefore operationally amnesic: each session begins with little durable knowledge of prior failures, repairs, preferences, or successful strategies. As a result, agents repeat the same mistakes and discard hard-won experience. The dominant fix is \emph{bespoke mem...
The PAU (Python API Understanding) benchmark is introduced, where models are provided with black-box, API-level access to code snippets, and inspiration is taken from the Asymmetric Actor Critic paradigm, frequently used in robot learning, to post-train models for interactive code understanding.
This work proposes an AI safety framework that binds worker agents to contracts specifying their permitted actions, and shows how these contracts can be enforced and specified by assigning distinct responsibilities to monitor agents and judges, and asymmetric computational resources to monitors and workers.
The proposed EngramRAG is an adaptive memory architecture coupling a low-latency Waking State reflex with an asynchronous background Dreaming State consolidation cycle, and introduces triple-source hybrid retrieval fusing dense vectors, BM25, and U-PPR via dynamic Reciprocal Rank Fusion (RRF).
CacheBack is a simple, robust, training-free instance of receiver conditioning based on the sender's attention weights that improves accuracy and reduces median task-completion latency on FanOutQA and shows comparable improvements across model families that span dense Transformers, Mamba-attention hybrids, and sliding-...
It is shown that reasoning concentrates a model's errors: the probability that two independently drawn wrong answers coincide rises in all ten dataset-scale comparisons, and in nine of nine after restricting both arms to the problems each gets wrong.
Large language models (LLMs) are increasingly integrated into educational settings, yet educators lack robust, standards-aligned tools to evaluate their effectiveness in K-12 science contexts. Existing benchmarks predominantly assess general language or advanced scientific reasoning, leaving a critical gap in understan...
Decentralized multi-agent path finding (MAPF) with communication requires agents to reach individual goals without collisions under partial observability. Learnable policies trained on expert data provide an effective approach to this problem. However, when several coordinated joint actions are valid in the same contex...
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
Machine Intelligence
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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