Supra Reasoning Summarizer — a tiny model to summarize thinking traces from coding agents
Summary
A small model designed to summarize the reasoning traces of coding agents, helping improve interpretability and efficiency.
Similar Articles
SuperThoughts: Reasoning Tokens in Superposition
SuperThoughts compresses consecutive chain-of-thought tokens into latent representations and decodes two tokens per step, achieving ~20–30% CoT length reduction with minimal accuracy loss on math reasoning benchmarks, while doubling inference throughput.
[Study/Models] Flint: Compressing Reasoning Without Breaking It
This study introduces section-aware compression for reasoning traces, training models to drop filler narration while preserving compute and verification spans, matching or exceeding original accuracy while using 2-3 times fewer tokens.
Inducing Reasoning Primitives from Agent Traces
Introduces Reasoning Primitive Induction, a method that mines successful ReAct traces to cluster recurrent reasoning moves into typed pseudo-tools, outperforming the original agent by tens of percentage points on benchmarks.
ScholarSum: Student-Teacher Abstractive Summarization via Knowledge Graph Reasoning and Reflective Refinement
ScholarSum is a hierarchical reflective graph-based framework for scientific abstractive summarization that emulates a student–teacher writing process. It uses a hierarchical knowledge graph to capture global structure, generates an initial draft, and iteratively refines it via evidence retrieval and teacher-like review to improve both fluency and factual faithfulness.
Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
This paper introduces a method for monitoring the reasoning process of Large Reasoning Models by analyzing probe trajectories—the evolution of a concept's probability across generated tokens. The approach uses temporal and signal-processing features from hidden representations to better predict future model behavior, achieving up to 95% AUROC with max-pooling.