Research map / Large language models / Agents, code and math

Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling

NeurIPS 2026 · Large language models › Agents, code and math · approach: Long-horizon LLM agents · 0 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes Recon, which scores synthesized reasoning traces by whether a frozen reconstruction model can recover the observed action from context plus the trace, used both to select traces (training-free) and as a GRPO reward for a reasoning synthesizer, for user modeling on conversational data.

Problem

Reasoning traces synthesized for user modeling are post-hoc rationalizations conditioned on the ground-truth action, so they may justify the action without capturing the latent decision process that produced it, and rejection sampling is infeasible in open-ended natural-language action spaces.

Approach

Score candidate rationalizations by action reconstruction fidelity: a frozen action model, given only context and the candidate trace, must regenerate the observed action; traces that reconstruct it best are selected (Recon-Select) or rewarded in GRPO (Recon-GRPO), with the synthesis prompt forbidden from revealing the action.

Key results

• Recon-Select achieves 54.7% overall win rate over Backward Synthesis with Qwen3-8B and 53.5% with GPT-5-mini.
• E2E-GRPO degrades downstream performance to a 38.4% overall win rate against Backward Synthesis.
• Recon-GRPO with Qwen3-4B reaches a 70.0% win rate over Backward Synthesis on PMQ.

Surprising findings

• Training the reasoning synthesizer end-to-end for action accuracy (E2E-GRPO) substantially degrades downstream user modeling performance to 38.4% win rate.
• Recon-Select reasoning from weaker Qwen3-8B does not significantly improve GPT-5-mini action generation, indicating limited transfer from weaker to stronger models.

Limitations

• Evaluated only in user simulation on eight personas across four domains; no verifiable domains like math or coding.
• Recon-GRPO explored in a single domain (PMQ) and gains vanish for Qwen3-8B (50.0%) when used as both policy and reconstructor.
• Added cost: Recon-Select requires 2N+1 LM calls; still operates on rationalizations conditioned on ground-truth action.

Experimental setup

Datasets: SCOTUS oral argument transcripts (Oyez) UK Hansard PMQ transcripts Lex Fridman and Tim Ferriss podcast transcripts Reddit r/AmItheAsshole comments via Arctic Shift

Benchmarks: SCOTUS PMQ Podcasts Reddit

Metrics: win rate vs Backward Synthesis (ties excluded) per-dimension win rate (style, intent, values) Cohen's kappa

Baselines: Backward Synthesis E2E-GRPO

Builds on: GRPO Advisor Models HumanLM STaR Trace Inversion

Closest papers by meaning

• Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens (ICLR 2026 · similarity 0.86)
• LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning (NeurIPS 2026 · similarity 0.85)
• Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration (ICML 2026 · similarity 0.85)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration (NeurIPS 2025 · similarity 0.85)
• CoRe: Collaborative Reasoning via Cross Teaching (ICML 2026 · similarity 0.84)
• RM-R1: Reward Modeling as Reasoning (ICLR 2026 · similarity 0.84)