TESSERA combines LLMs as local policy and evaluator with MCTS on knowledge graphs to compose mechanistic drug-disease explanations.
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Inverse-RPO derives two variance-aware prior-based UCT policies from UCB-V that outperform PUCT on benchmarks with no extra cost.
LLMs can forecast GPU kernel performance accurately enough to serve as selective surrogates, allowing kernel searches to consider more candidates and recover faster kernels under fixed GPU evaluation budgets.
CPR uses query-level conformal calibration over path scores and a PUCT-trained RCVNet to achieve valid coverage guarantees and smaller prediction sets in KGQA, reporting 45% higher empirical coverage and 52% smaller sets than prior conformal baselines.
SimpleTES scales test-time evaluation in LLMs to discover state-of-the-art solutions on 21 scientific problems across six domains, outperforming frontier models and optimization pipelines with examples like 2x faster LASSO and new Erdos constructions.
TreeCoder improves LLM code generation accuracy by representing decoding as an optimizable tree search over programs with first-class constraints for syntax, style, and execution, outperforming baselines on MBPP and SQL-Spider.
Aristotle reaches gold-medal-equivalent performance on 2025 IMO problems via integrated Lean proof search, informal lemma formalization, and a dedicated geometry solver.
Modified OCL search integrates generative rollouts and learned heuristics for efficient inference in planning models across combinatorial domains.
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