A survey of 172 open educational datasets from 204 papers across LAK, EDM, and AIED conferences reveals trends, 143 previously uncatalogued datasets, field gaps, and an 8-item PRACTICE checklist for better data publication.
2nd International Conference on Foundation and Large Language Models,
10 Pith papers cite this work. Polarity classification is still indexing.
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Metacognitive self- and co-regulation loops improve LLM agent performance in engineering design by mitigating fixation and enabling better exploration of design options.
The authors define general non-functional rules for C modules, propose an interface contract language, implement a Frama-C checker plugin, and demonstrate verification on two Scania truck codebases alongside ACSL functional contracts.
PUMA detects reasoning-level semantic redundancy to enable early exit in chains of thought, achieving 26.2% average token reduction across five LRMs and five benchmarks while preserving accuracy and CoT quality.
Adversarial competition between attacker and defender teams generates diverse multi-turn conversational data that improves LLM performance on secure code generation benchmarks by 18-29%.
CAT uses intrinsic confidence signals in preference optimization to adapt reasoning length in LRMs, outperforming uniform compression baselines on accuracy across benchmarks.
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
SAT reduces reasoning tokens by up to 40% across multiple large reasoning models and benchmarks by adaptively pruning steps based on difficulty while maintaining or improving accuracy.
ReBias-Lens shows LLM self-reflection produces layer-wise smoothing of global valence fluctuations that reduces behavioral bias overall, yet selectively locks in and amplifies certain category-specific biases.
citing papers explorer
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Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
PUMA detects reasoning-level semantic redundancy to enable early exit in chains of thought, achieving 26.2% average token reduction across five LRMs and five benchmarks while preserving accuracy and CoT quality.
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CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models
CAT uses intrinsic confidence signals in preference optimization to adapt reasoning length in LRMs, outperforming uniform compression baselines on accuracy across benchmarks.