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19,710 papers
MemPlan is proposed, a memory-conditioned PDDL planning framework for partially observable interactive text environments, which improves task completion and step efficiency over language-only and PDDL-aware baselines, while substantially reducing online token cost compared with LLM-planning baselines such as ReAct.
Model fusion aims to integrate the capabilities of multi-expert models into a single model. Existing evaluations offer limited coverage of reasoning with long chains of thought and tool use, while strong average performance can mask substantial losses in individual expert capabilities. We introduce FusionEval, a benchm...
Many Indian banks serve customers through FAQ pages and scripted chatbots. Assistants based on language models could respond faster and carry out requests such as blocking a card, but only if they give correct information and follow the bank's rules. These rules prescribe a procedure, a set of required steps, such as o...
This work presents the first digital assistant named Lighthouse, the first digital assistant named Lighthouse--commonly called a chatbot--for exploring the Civil Rights period of the United States using data from South Carolina.
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This work proposes Code Comprehensor, a hybrid retrieval-reasoning framework combining CodeGraph, a code dependency callgraph, CodeScribe, which uses code-aware LLMs to generate hierarchical documentation across repository call graphs, and CodeContrast, a CLIP-style dual-encoder model trained on CodeScribe output to le...
This work takes the context and the teacher's answer as input, and generates the reasoning trace ourselves, in the style of the student's own model family, suspect that reasoning written in the student's family style is easier for it to learn from.
This work introduces WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events, and reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.
Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely available for years, yet reported accuracy assumes that these examples were not in the training data. We test this assumption for open VLMs on SLAKE-En, PathVQA, and VQA-RAD, and on a public...
This work introduces Projection agents, a GCO methodology built on the Wolpertinger framework and adapted to a shared GNN-based latent space for observations and actions, and releases LaGCO-RL, a Python library that automates latent action-space construction and supports existing RL-GCO solutions, promoting reproducibi...
The results suggest that training explicitly for saliency is a promising route towards chain-of-thought reasoning that is more faithful to the model's actual decision process.
This paper defines this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior data.
This work introduces SleepMaMi, a Sleep Foundation Model engineered to master both hour-long sleep architectures and fine-grained signal morphologies, demonstrating superior generalizability and label-efficient adaptation for clinical sleep analysis.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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