SteER introduces an interactive framework for deep research with LLMs that uses cost-benefit analysis for user control at decision points and shows improved alignment over baselines.
URL https: //aclanthology.org/2025.acl-demo.14/
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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A merged-token graph of many LM generations helps users compare output diversity, while raw lists remain better for detail-oriented distributional questions.
ThinkBooster supplies a modular library, joint performance-efficiency benchmark, and deployable proxy for test-time compute scaling of LLM reasoning on math and coding tasks.
citing papers explorer
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An Interactive Paradigm for Deep Research
SteER introduces an interactive framework for deep research with LLMs that uses cost-benefit analysis for user control at decision points and shows improved alignment over baselines.
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Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
A merged-token graph of many LM generations helps users compare output diversity, while raw lists remain better for detail-oriented distributional questions.
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ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning
ThinkBooster supplies a modular library, joint performance-efficiency benchmark, and deployable proxy for test-time compute scaling of LLM reasoning on math and coding tasks.