Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
Test-time recursive thinking: Self-improvement without external feedback
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EvoLib improves black-box LLM test-time performance by maintaining an evolving, self-scored library of reusable skills and insights, without parameter updates or ground-truth feedback.
MarsTSC is a VLM agentic system with generator, reflector, and modifier roles that iteratively refines a knowledge bank to improve few-shot multimodal time series classification and produce human-readable explanations.
TTSP samples and filters multiple zoom-in exploration traces and iteratively consolidates validated observations into an Evidence Ledger, improving fine-grained multimodal reasoning on V* Bench, HR-Bench, TreeBench, and MME-RealWorld-Lite.
LLMs improve reasoning in math, science, and coding by generating and self-filtering their own training data through cycle-consistency, factuality, and correctness checks on unlabeled prompts.
citing papers explorer
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Agentic-imodels: Evolving agentic interpretability tools via autoresearch
Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
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Test-Time Learning with an Evolving Library
EvoLib improves black-box LLM test-time performance by maintaining an evolving, self-scored library of reusable skills and insights, without parameter updates or ground-truth feedback.
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Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
MarsTSC is a VLM agentic system with generator, reflector, and modifier roles that iteratively refines a knowledge bank to improve few-shot multimodal time series classification and produce human-readable explanations.
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Test-time Scaling over Perception: Resolving the Grounding Paradox in Thinking with Images
TTSP samples and filters multiple zoom-in exploration traces and iteratively consolidates validated observations into an Evidence Ledger, improving fine-grained multimodal reasoning on V* Bench, HR-Bench, TreeBench, and MME-RealWorld-Lite.
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Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
LLMs improve reasoning in math, science, and coding by generating and self-filtering their own training data through cycle-consistency, factuality, and correctness checks on unlabeled prompts.