Failed reasoning traces encode recoverability structure for test-time routing and post-training analysis
A new arXiv paper argues that failed reasoning traces from post-trained LLMs contain exploitable signal about whether failures are recoverable via resampling or require structural intervention. The authors derive three trajectory features from the distributional signature of failed rollouts (not their text content) that cluster failures into stable regimes and characterize failure topography across post-training methods with 84.3% accuracy. A training-free routing rule built on these features lifts rescue rates by +12.2% on a deployment-relevant hard subset, and the features transfer across model families. The work reframes failed traces as diagnostic objects rather than discarded data, with implications for inference-time compute allocation and post-training analysis.
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Probe Trajectories Reveal Reasoning Dynamics in Large Reasoning Models
This paper investigates whether hidden representations of Large Reasoning Models (LRMs) can predict future model behavior by analyzing probe trajectories—the continuous evolution of concept probabilities across Chain-of-Thought reasoning tokens. The authors find that temporal trajectory features (volatility, trend, steady-state) significantly outperform single static probes, with max-pooling achieving up to 95% AUROC across safety and mathematics domains. Two methodological insights are offered: template-based training data matches dynamically generated responses in quality, and pooling strategy is critical to probe performance. The work positions probe trajectories as a complementary safety monitoring framework for LRMs where CoT faithfulness cannot be assumed.
Trustworthiness audit finds alignment regressions in reasoning models converted from instruction-tuned LLMs
A systematic study audits whether converting instruction-tuned LLMs into reasoning models via SFT, RL-based post-training, or distillation preserves alignment behaviors such as safe refusal, bias avoidance, and privacy protection. Across six trustworthiness dimensions, the authors find consistent alignment regressions—including increased toxicity, amplified stereotyping, miscalibrated refusal, and privacy leakage—even as reasoning benchmark scores improve. The regressions are quantified via KL divergence from the instruction-tuned baseline, suggesting behavioral drift is a systematic byproduct of reasoning post-training. The paper argues trustworthiness metrics should be reported alongside reasoning capability gains.
LongTraceRL: Reinforcement Learning for Long-Context Reasoning via Search Agent Trajectories and Rubric Rewards
LongTraceRL is a new RL training framework for improving long-context reasoning in LLMs, addressing limitations of existing RLVR methods. It constructs challenging training data using multi-hop questions from knowledge graph random walks and tiered distractors derived from search agent trajectories (high-confusability: read but uncited; low-confusability: seen but unopened). A rubric reward provides entity-level process supervision along reasoning chains, applied only to correct responses to prevent reward hacking. Experiments across three LLMs (4B–30B parameters) on five long-context benchmarks show consistent improvements over strong baselines.
Provenance-grounded gating and adaptive recovery improve synthetic post-training data curation
A controlled study examines two underexplored practices in synthetic post-training data pipelines: grounding filtering signals in source provenance and systematically recovering rejected samples rather than discarding them. Using adversarially injected corpora as ground-truth failure labels, the authors find that exact source provenance improves faithfulness gating for stronger judges, that hallucination and reward gates reject largely disjoint populations (making both necessary), and that adaptive recovery via failure diagnosis and targeted regeneration outperforms naive resampling. Generator scale is the primary driver of downstream fine-tuning quality, with filtration and recovery contributing meaningfully but secondarily.
Synthetic linguistic reasoning traces improve low-resource machine translation via in-context learning
Researchers propose a pipeline that generates step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks to assist LLMs in translating extremely low-resource languages. Evaluated on Xibe and Chintang across ICL, SFT, and RFT settings, the traces prove most effective as inference-time guidance rather than training data. Models can leverage reliable grammatical analyses at inference time but struggle to learn to generate accurate traces themselves, identifying trace generation quality as the key bottleneck.
ExpRL: RL-based mid-training using human QA data as reward scaffolds for LLM reasoning
ExpRL proposes an automated approach to LLM mid-training that replaces manually curated reasoning traces with large corpora of human-written QA data used as reward scaffolds rather than imitation targets. Reference solutions are hidden from the policy and used only to construct problem-specific grading rubrics, enabling dense process-level rewards that reinforce partial progress and intermediate reasoning steps. On challenging math reasoning benchmarks, ExpRL outperforms SFT, sparse-reward GRPO, and self-distillation as an RL initialization strategy, with additional mixed-domain experiments suggesting broader applicability.
Framework for quantifying faithful confidence expression in large reasoning models
A new arXiv preprint introduces a framework to measure faithful calibration (FC) in large reasoning models (LRMs)—the alignment between a model's intrinsic confidence and its linguistically expressed confidence. The authors analyze linguistic decisiveness against three internal uncertainty sources (token probabilities, hidden states, sampled response consistency) and introduce prefix-conditioned sampling to handle structural variation in chain-of-thought traces. Applying the framework across leading models, they find FC is a significant and distinct failure mode for LRMs: extended reasoning traces do not automatically improve calibration, prompt interventions that help non-reasoning models fail in the reasoning setting, and different confidence estimators produce divergent assessments of the same traces.
Reroute: Training-free recoverable visual token routing for vision-language models
A new arXiv preprint proposes Reroute, a training-free plug-in that replaces the standard rank-and-remove visual token pruning paradigm in VLMs with a recoverable routing mechanism. Instead of permanently discarding low-ranked tokens, Reroute defers them to re-enter the candidate pool at later decoder stages, addressing the problem that token importance shifts across decoder depth. Evaluated on LLaVA-1.5 and Qwen backbones augmented with FastV, PDrop, and Nüwa pruning methods, Reroute improves grounding performance under aggressive token reduction without sacrificing general VQA accuracy. The approach preserves the theoretical compute and KV-cache budget of the underlying pruning method.