Tianlong Chen

Long-context reasoning remains computationally expensive for large language models due to the quadratic complexity of attention over text tokens. Visual-text compression offers a promising alternative by rendering text into images and processing them with vision-language models, often reducing token usage. However, existing approaches apply uniform compression regardless of query relevance, potentially sacrificing precision where detailed extraction is required. We present SEER, a framework that learns to select query-relevant images through visual scanning and retrieve textual content only where needed, combining the efficiency of visual compression with the precision of text-based reasoning. Through supervised fine-tuning on tool-interaction trajectories, SEER learns adaptive tool invocation for selection and retrieval. Experiments on long-context benchmarks show that SEER improves extraction precision through selective text retrieval while retaining average prompt-token savings relative to full-text baselines. On LongBench, SEER achieves 51.11\% average accuracy, outperforming the visual-text baseline Glyph-9B by 2.33 points and Qwen3-8B by 3.49 points. Code can be accessed at https://github.com/jiaweixu98/SEER

Unlearning ensures LLM compliance by removing the influence of private or copyrighted training data. However, since LLM models typically undergo post-training compression, like quantization, in practical deployment, it has been observed that the unlearning effect can be substantially weakened, with the forgetting behavior degrading more severely than that of model utility. This paper proposes a quantization-robust unlearning framework that makes forgetting robust to quantization while maintaining overall model utility. We analyze this gap through the lens of loss landscape. Specifically, our analysis reveals a curvature-based criteria that pinpoints sensitive weights in the unlearned model that leads to both non-robust forgetting and reduced utility. We therefore propose sensitivity-guided noisy regularization, which is applied on the sensitive parameters to steer the model convergence towards a smoother minima of uniformly low forget and retain losses. Balancing unlearning and utility, we further propose forget-critical optimization, which updates only forget-critical layers, preserving most of the network to retain useful knowledge. Extensive experiments on the MUSE and TOFU benchmarks across multiple LLM unlearning algorithms show that our approach achieves substantially more quantization-resilient forgetting while maintaining utility.

Large Language Models (LLMs) can produce complex reasoning chains, offering a window into their decision-making processes. However, verifying the quality (e.g., faithfulness and correctness) of these intermediate steps is a critical, unsolved challenge. Current auditing methods usually rely on a centralized auditor, either human or verifier model, to inspect full reasoning traces. This paper addresses three key challenges in those methods. **Robustness**: Centralized systems are single points of failure, vulnerable to attacks and systemic bias. **Scalability**: The lengthiness and complexity of reasoning traces create a severe bottleneck for human auditors. **IP confidentiality**: Model providers risk intellectual property theft or model distillation when exposing complete reasoning traces. To overcome these barriers, we introduce TRUST, a crowdsourcing framework for auditing LLM reasoning. TRUST makes the following contributions: *First*, it establishes a consensus mechanism among a diverse set of auditors that removes reliance on any *single trusted auditor*: under the modeled seat-corruption process the trace-level failure bound holds up to a 20% adversarial seat fraction, and empirically TRUST retains 69.0% accuracy with one compromised seat per segment, where centralized baselines collapse to ~20%. *Second*, it introduces a scalable decomposition method that transforms reasoning traces into hierarchical directed acyclic graphs, enabling atomic reasoning steps to be audited in parallel by a crowd of independent auditors. *Third*, the framework limits exposure by distributing only partial segments of the reasoning trace to auditors. We provide theoretical guarantees for the security and economic incentives. Experiments across multiple LLMs (e.g., GPT-OSS, DeepSeek-r1, Qwen) and reasoning tasks (e.g., math, medicine, science, and humanities) demonstrate that TRUST is highly effective at identifying reasoning flaws and is significantly more resilient to corrupted and adversarial auditors than centralized baselines. Our work introduces an auditing framework for reasoning AI systems, enabling safer and secure deployment in high-stakes settings.