SpatialGrammar provides a grid-based DSL and compiler that lets LLMs generate collision-free 3D indoor scenes more reliably than raw-coordinate or code-based approaches.
Infobench: Evaluating instruction following ability in large language models.arXiv preprint arXiv:2401.03601
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SAGE is a new multi-agent benchmark that formalizes service SOPs as dynamic dialogue graphs to measure LLM agents on logical compliance and path coverage, uncovering an execution gap and empathy resilience across 27 models in 6 scenarios.
Personalized deep research systems need evaluation with real users because LLM judges overlook nuanced errors that matter to researchers.
Experiments reveal that LLMs follow instructions at rates from 1% to 99% when opposed by hardcoded conflicting patterns, with robustness tied to output diversity and alignment with model priors rather than general capability.
FusionRoute augments token-level expert routing with a trainable complementary logit generator to expand the policy class and recover optimal decoding under mild conditions, outperforming prior collaboration and merging methods on reasoning and generation benchmarks.
PRIME enables online process reward model updates in LLM RL using implicit rewards from rollouts and outcome labels, yielding 15.1% average gains on reasoning benchmarks and surpassing a stronger instruct model with 10% of the data.
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
citing papers explorer
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SpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation
SpatialGrammar provides a grid-based DSL and compiler that lets LLMs generate collision-free 3D indoor scenes more reliably than raw-coordinate or code-based approaches.
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SAGE: A Service Agent Graph-guided Evaluation Benchmark
SAGE is a new multi-agent benchmark that formalizes service SOPs as dynamic dialogue graphs to measure LLM agents on logical compliance and path coverage, uncovering an execution gap and empathy resilience across 27 models in 6 scenarios.
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Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
Personalized deep research systems need evaluation with real users because LLM judges overlook nuanced errors that matter to researchers.
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Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
Experiments reveal that LLMs follow instructions at rates from 1% to 99% when opposed by hardcoded conflicting patterns, with robustness tied to output diversity and alignment with model priors rather than general capability.
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Token-Level LLM Collaboration via FusionRoute
FusionRoute augments token-level expert routing with a trainable complementary logit generator to expand the policy class and recover optimal decoding under mild conditions, outperforming prior collaboration and merging methods on reasoning and generation benchmarks.
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Process Reinforcement through Implicit Rewards
PRIME enables online process reward model updates in LLM RL using implicit rewards from rollouts and outcome labels, yielding 15.1% average gains on reasoning benchmarks and surpassing a stronger instruct model with 10% of the data.
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Intelligent Drill-Down: Large Language Model-Driven Drill-Down Technique for Human-AI Collaborative Visual Exploration
An LLM-based framework recommends drill-down paths in visual analytics by approximating a greedy algorithm, interpreting user intent, and managing exploration branches to reduce cognitive load.
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