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16,124 papers
This paper evaluates six strong browser-use agents, three GUI-only agents that see only screenshots, and humans, evaluating the construction of challenging instances from tasks agents already solve, turning difficulty into a programmable property of the environment.
Neighbor information improves individual prediction in all 16 public-data settings and pooled collective forecasts on held-out questions, although collective gains depend on transfer conditions.
Lookahead-R is proposed, a planning-based framework that reformulates tool retrieval as a resource-constrained sequential decision-making problem that introduces a lightweight execution-aware surrogate world model that jointly predicts tool execution success, latency cost, and semantic utility—without invoking real API...
TRACE is presented, a deployable tree-relational enhancement framework for oncology LLMs that separates expensive offline structure learning from lightweight online inference, and suggests that explicit, updatable medical structure is a practical path toward more accurate and auditable oncology LLM deployment.
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A multi-agent framework in which a story agent, an image agent, and a critic agent collaborate to produce fake social media posts that plausibly counter true news is introduced, providing a foundation for developing robust defenses against social media fake news.
Memory Adaptation for Task-Conditioned Execution (MATE) is introduced, a deterministic post-retrieval procedure that converts trajectories into execution-oriented memory and supports memory adaptation as a distinct stage between retrieval and embodied execution.
LLM agents can make unsafe tool calls even when instructed to behave safely. Existing defenses constrain agents before execution, modify tool inputs/outputs, or rely on LLM judges; these approaches may depend on model behavior or block unsafe actions without helping the agent recover. We argue that the execution enviro...
This work formalizes the task as structured prediction of (skill, level) pairs from free text and asks three questions: how accurately can text be mapped onto SFIA's closed vocabulary, which strategies reliably predict the level alongside the skill, and do agentic designs improve on simpler retrieval and prompting?
Training a language model on data with a narrow flaw can sometimes make the model broadly misaligned. Inspecting the data at face value often does not settle whether it will emerge, and today it is caught only after training, by auditing the resulting model. To complement post-hoc audits, we introduce Alignment Forecas...
It is shown that contrary to previous studies, perturbations can mitigate bias and hallucination in some LLMs over other models, and it's found that Claude 3 is more effective for the tasks represented in most datasets, whereas models like GPT3.5 exhibit varying levels of adequacy.
Large language model (LLM) judges are often used as rewards to train policies on objectives that deterministic verifiers cannot capture. However, these rewards are often collapsed to pass/fail ({0, 1}), which reports the verdict but not how well a response met each criterion. We model each pass/fail verdict as a score...
CALIBRA is evaluated on a documented three-cohort semi-synthetic benchmark with controlled distribution shift, informative missingness, and sealed target patients to verify the method and reproducible pipeline under controlled shift, but do not establish clinical effectiveness.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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
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