PDF Understanding

Long-context models (LCMs) have demonstrated great potential in processing long sequences, facilitating many real-world applications. The success of LCMs can be attributed to their ability to locate implicit critical information within the context for further prediction. However, recent research reveals that LCMs are often susceptible to contextual noise, i.e., irrelevant tokens, that can mislead model attention. In this paper, we conduct a fine-grained analysis of the context noise and propose an effective metric, the Integrated Gradient (IG) score, to detect and quantify the noise information within the context. Our findings reveal that even simple mitigation of detected context noise can substantially boost the model's attention on critical tokens and benefit subsequent predictions. Building on this insight, we propose Context Denoising Training (CDT), a straightforward yet effective training strategy that improves attention on critical tokens while reinforcing their influence on model predictions. Extensive experiments across four tasks, under both context window scaling and long-context alignment settings, demonstrate the superiority of CDT. Notably, when trained with CDT, an open-source 8B model can achieve performance (50.92) comparable to GPT-4o (51.00).

Videos contain rich information but also high redundancy, as consecutive frames often share similar backgrounds and predictable motions. Current video-language models (VLMs) are unable to exploit this redundancy and therefore perform a significant amount of superfluous computation, processing thousands of patch tokens even when little new information is present. What is missing is an on-the-fly, model-agnostic signal of temporal predictability to decide whether tokens carry unpredictable information that merits computation. We propose SURGE, a training-free and backbone-agnostic method that measures surprise in token space. Surprise scores are defined by the prediction error of each token from its recent history; high-surprise tokens are retained, while predictable ones are pruned. Aggregating scores over time produces a surprise curve that highlights key events, which can be further refined with CLIP-based query relevance to form a compact spatio-temporal mask. Experiments on multiple video understanding benchmarks show that SURGE reduces tokens by up to 7$\times$ and prefill cost by 86–98\%, while maintaining accuracy within $\pm$1 point of full-token baselines. By aligning computation with novelty, SURGE enables video VLMs to handle long contexts efficiently and without retraining.

Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexity of papers. A central challenge lies in detecting and resolving inconsistencies across text, figures, tables, and equations, issues that are often subtle, domain-specific, and ultimately undermine clarity, reproducibility, and trust. Existing benchmarks overlook this issue, either isolating single modalities or relying on synthetic errors that fail to capture real-world complexity. We introduce PRISMM-Bench (Peer-Review-sourced Inconsistency Set for Multimodal Models), the first benchmark grounded in real reviewer-flagged inconsistencies in scientific papers. Through a multi-stage pipeline of review mining, LLM-assisted filtering and human verification, we curate 262 inconsistencies from 242 papers. Based on this set, we design three tasks, namely inconsistency identification, remedy and pair matching, which assess a model's capacity to detect, correct, and reason over inconsistencies across different modalities. Furthermore, to address the notorious problem of \emph{choice-only shortcuts} in multiple-choice evaluation, where models exploit answer patterns without truly understanding the question, we further introduce structured JSON-based answer representations that minimize linguistic biases by reducing reliance on superficial stylistic cues. We benchmark 21 leading LMMs, including large open-weight models (GLM-4.5V 106B, InternVL3 78B) and proprietary models (Gemini 2.5 Pro, GPT-5 with high reasoning). Results reveal strikingly low performance (26.1–54.2\%), underscoring the challenge of multimodal scientific reasoning and motivating progress towards trustworthy scientific assistants. We provide the source code and dataset viewer in the appendix, and will release the full source code, dataset, and annotation tool publicly upon acceptance.

Self-supervised learning (SSL) has emerged as a powerful pretraining strategy to learn transferable representations from unlabeled data. Yet, it remains unclear how long SSL models should be pretrained for such representations to emerge. Contrary to the prevailing heuristic that longer pretraining translates to better downstream performance, we identify a transferability trade-off: across diverse SSL settings, intermediate checkpoints often yield stronger out-of-domain (OOD) generalization, whereas additional pretraining primarily benefits in-domain (ID) accuracy. From this observation, we hypothesize that SSL progresses through learning phases that can be characterized through the lens of critical periods (CP). Prior work on CP has shown that supervised learning models exhibit early phases of high plasticity, followed by a consolidation phase where adaptability declines but task-specific performance keeps increasing. Since traditional CP analysis depends on supervised labels, for SSL we rethink CP in two ways. First, we inject deficits to perturb the pretraining data and measure the quality of learned representations via downstream tasks. Second, to estimate network plasticity during pretraining we compute the Fisher Information matrix on pretext objectives, quantifying the sensitivity of model parameters to the supervisory signal defined by the pretext tasks. We conduct several experiments to demonstrate that SSL models do exhibit their own CP, with CP closure marking a sweet spot where representations are neither underdeveloped nor overfitted to the pretext task. Leveraging these insights, we propose CP-guided checkpoint selection as a mechanism for identifying intermediate checkpoints during SSL that improve OOD transferability. Finally, to balance the transferability trade-off, we propose CP-guided self-distillation, which selectively distills layer representations from the sweet spot (CP closure) checkpoint into their overspecialized counterparts in the final pretrained model.

Recurrent large language models (Recurrent LLMs) offer linear computational complexity as efficient alternatives to quadratic self-attention-based LLMs (Self-Attention LLMs). However, Recurrent LLMs underperform on long-context tasks due to limited fixed-size memory. Previous research focused on architectural innovations to enhance memory capacity, but failed to match Self-Attention LLM performance. We argue this limitation stems from processing entire contexts at once being ill-suited for Recurrent LLMs. We propose Smooth Reading, a co-design of recurrent architecture and inference method. It introduces a end-to-end multi-round inference method that processes context incrementally and iteratively summarizes information, reducing memory demands. Methodologically, we reveal architecture-inference interactions play an important role for performance, efficiency and scalability, shedding light on future Recurrent LLM design. Besides, our method substantially bridges the performance gap between Recurrent and Self-Attention LLMs on long-context tasks while preserving efficiency advantages. Smooth Reading boosts SWA-3B-4k from 5.68% lower to 3.61% higher performance than Self-Attention LLMs on LongBench, while maintaining 2.5× faster training and 2× faster inference at 64k context.

Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce a new simulation framework, LH-Deception, for a systematic, empirical quantification of deception in LLMs under extended sequences of interdependent tasks and dynamic contextual pressures. LH-Deception is designed as a multi-agent system: a performer agent tasked with completing tasks and a supervisor agent that evaluates progress, provides feedback, and maintains evolving states of trust. An independent deception auditor then reviews full trajectories to identify when and how deception occurs. We conduct extensive experiments across 11 frontier models, spanning both closed-source and open-source systems, and find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust. Qualitative analyses further reveal emergent, long-horizon phenomena, such as ``chains of deception", which are invisible to static, single-turn evaluations. Our findings provide a foundation for evaluating future LLMs in real-world, trust-sensitive contexts.