Fine-tuning LLMs on an unseen language teaches syntax but fails to transfer semantic competence, leaving Python with up to a 19% performance advantage and no tested intervention closing the gap.
Livecodebench pro: How do olympiad medalists judge llms in competitive programming?
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CodeSpecBench shows LLMs achieve at most 20.2% pass rate on repository-level executable behavioral specification generation, revealing that strong code generation does not imply deep semantic understanding.
TESSY creates stylistically consistent synthetic data via teacher-student token interleaving, yielding 11.25% and 6.68% gains on code benchmarks where pure teacher data causes 3.25% and 10.02% drops.
OpenDeepThink uses Bradley-Terry aggregation of LLM pairwise judgments to rank and evolve parallel reasoning traces, improving Gemini 3.1 Pro Codeforces Elo by 405 points over eight rounds.
On Codeforces problems, independent k-shot sampling achieves better accuracy-cost and accuracy-query tradeoffs than agentic reasoning, even with prompt caching.
Seed1.8 is a new foundation model that adds unified agentic capabilities for search, code execution, and GUI interaction to existing LLM and vision strengths.
citing papers explorer
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Syntax Without Semantics: Teaching Large Language Models to Code in an Unseen Language
Fine-tuning LLMs on an unseen language teaches syntax but fails to transfer semantic competence, leaving Python with up to a 19% performance advantage and no tested intervention closing the gap.
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CodeSpecBench: Benchmarking LLMs for Executable Behavioral Specification Generation
CodeSpecBench shows LLMs achieve at most 20.2% pass rate on repository-level executable behavioral specification generation, revealing that strong code generation does not imply deep semantic understanding.
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How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
TESSY creates stylistically consistent synthetic data via teacher-student token interleaving, yielding 11.25% and 6.68% gains on code benchmarks where pure teacher data causes 3.25% and 10.02% drops.
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OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
OpenDeepThink uses Bradley-Terry aggregation of LLM pairwise judgments to rank and evolve parallel reasoning traces, improving Gemini 3.1 Pro Codeforces Elo by 405 points over eight rounds.
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When Independent Sampling Outperforms Agentic Reasoning
On Codeforces problems, independent k-shot sampling achieves better accuracy-cost and accuracy-query tradeoffs than agentic reasoning, even with prompt caching.
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Seed1.8 Model Card: Towards Generalized Real-World Agency
Seed1.8 is a new foundation model that adds unified agentic capabilities for search, code execution, and GUI interaction to existing LLM and vision strengths.