SeedHijack is a blind, integrity-preserving PRNG hijacking attack that amplifies LLM watermark z-scores up to 2.42x while evading all tested content-side statistical detectors across three schemes and models.
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Qwen Technical Report
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abstract
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models.
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- abstract Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a mult
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representative citing papers
LongAct benchmark evaluates long-horizon household task execution from free-form instructions; HoloMind agent raises performance but top VLMs still reach only 59% goal completion and 16% full-task success.
The upper-tail accumulation scale derived from the gap-counting function N_n sets the critical inverse temperature for softmax attention concentration, unifying prior conflicting laws as special cases of different N_n.
IRIS-14B is the first LLM trained explicitly for GIMPLE-to-LLVM IR translation and outperforms much larger models by up to 44 percentage points on real-world C code.
A classifier trained only on transformer fine-tuning data detects an invariant memorization signature that transfers to Mamba, RWKV-4, and RecurrentGemma with AUCs of 0.963, 0.972, and 0.936.
LiveBench is a contamination-limited LLM benchmark with auto-scored challenging tasks from recent sources across math, coding, reasoning and more, where top models score below 70%.
OSWorld provides the first unified real-computer benchmark for open-ended multimodal agent tasks, exposing large performance gaps between humans and state-of-the-art LLM/VLM agents.
A new sensitivity-labeled test collection is released from Enron emails with crowdsourced queries, relevance judgments, and LLM extensions for evaluating sensitivity-aware search.
Moral Trolley Arena shows frontier LLMs produce composite moral preferences that are compressed rather than additive functions of calibrated component act strengths across Moral Foundations Theory.
Introduces ChiSafe-PAS, a 1,897-prompt human-annotated Chinese adversarial benchmark for LLM safety with 3-class labels, 9-category obfuscation taxonomy, and domain coverage in self-harm, drugs, fraud, and satire.
EvoGM uses a dual-generator architecture with cycle-consistent learning on winner-loser pairs from search history to optimize LLM merging coefficients inside a multi-round evolutionary pipeline and reports outperformance over baselines on seen and unseen tasks.
FTibSuite provides human-verified multimodal corpora, Tibetan-adapted benchmarks with quality controls, and a baseline VLM showing gains on tasks like MMBench while preserving Chinese capabilities.
VisAnalog is a new controlled benchmark showing VLMs substantially underperform humans on visual concept transfer under one- to four-step deterministic transformations, with relation inference as the main failure mode.
Proposes an equation-anchored tool-use method for MLLMs that writes the pinhole back-projection equation in Chain-of-Thought and substitutes retrieved camera intrinsics and depths to achieve robustness in 3D object detection and visual grounding under rescaled intrinsics.
Incantation is the first video world model to use per-frame natural language conditioning for simultaneous multi-entity control and concept-level cross-entity transfer in interactive video generation.
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
RISE is an inference-time semantic reranking framework that refines low-confidence predictions in rhetorical role labeling using contrastively learned label representations, delivering an average +9.15 macro-F1 gain on hard examples across eight datasets and seven models.
DCDM replaces positional blocks with learnable semantic chunks via differentiable Chunking Attention, yielding consistent gains over block and unstructured diffusion baselines up to 1.5B parameters.
ClawForge is a generator framework that creates reproducible executable benchmarks for command-line agents under state conflict, with ClawForge-Bench showing frontier models reach at most 45.3% strict accuracy and that state inspection drives most performance gaps.
BlockVLA accelerates autoregressive VLA models by 3.3x using block diffusion finetuning, with faster training convergence and better early performance on long-horizon robotic tasks.
Pretrained LLMs adapted via convolutional projections and LoRA act as efficient frozen backbones for sensor-based human activity recognition, delivering strong data efficiency and cross-dataset transfer.
ReCrit frames critic interaction as a correctness-transition problem and uses quadrant-based RL rewards to improve LLM performance on scientific reasoning benchmarks by rewarding corrections and robustness while penalizing sycophancy.
V-ABS is an action-observer beam search method with entropy-based adaptive weighting and an 80k-sample SFT dataset that delivers 19.7% average gains on visual reasoning tasks for MLLMs.
LLM agents reach only 50.6% accuracy on chemical cost estimation within 25% error even with tools, dropping with noise due to parsing, pack selection, and tool-use failures.
citing papers explorer
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All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.
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See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection
ForeSight lets VLMs use low-level visual cues and mask-based visual feedback within an RL loop to reason more accurately, with the 7B model beating same-scale peers and some closed-source SOTA on a new benchmark.
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Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
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The Override Gap: A Magnitude Account of Knowledge Conflict Failure in Hypernetwork-Based Instant LLM Adaptation
Knowledge conflicts in hypernetwork LLM adaptation stem from constant adapter margins losing to frequency-dependent pretrained margins; selective layer boosting and conflict-aware triggering raise deep-conflict accuracy to 71-72.5% on Gemma-2B and Mistral-7B.
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Mixture of Heterogeneous Grouped Experts for Language Modeling
MoHGE achieves standard MoE performance with 20% fewer parameters and balanced GPU utilization via grouped heterogeneous experts, two-level routing, and specialized auxiliary losses.
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RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices
RealBench is a new repo-level code generation benchmark that adds UML diagrams to natural language specs, showing LLMs struggle more at full repositories, create modules with errors, and perform best with whole-repo generation on small projects versus module-by-module on complex ones.
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GazeVLA: Learning Human Intention for Robotic Manipulation
GazeVLA pretrains on large human egocentric datasets to capture gaze-based intention, then finetunes on limited robot data with chain-of-thought reasoning to achieve better robotic manipulation performance than baselines.
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Latent Denoising Improves Visual Alignment in Large Multimodal Models
A latent denoising objective with saliency-aware corruption and contrastive distillation improves visual alignment and corruption robustness in large multimodal models.
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If you're waiting for a sign... that might not be it! Mitigating Trust Boundary Confusion from Visual Injections on Vision-Language Agentic Systems
LVLM-based agents exhibit trust boundary confusion with visual injections and a multi-agent defense separating perception from decision-making reduces misleading responses while preserving correct ones.
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Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
AdaLeZO uses a non-stationary multi-armed bandit to adaptively allocate perturbation budget across layers in zeroth-order optimization and applies inverse probability weighting to reduce variance while preserving unbiased gradients, delivering 1.7x-3.0x wall-clock speedup on LLaMA and OPT models.
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Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages
Phoneme-level analysis of ASR on Archi and Rutul shows data scarcity explains recognition errors better than phonological complexity, with language-specific adaptations improving wav2vec2 performance.
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Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
By requiring and using highly discriminative LLM text features, the work enables the first effective one-step text-conditioned image generation with MeanFlow.
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AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models
AnchorRefine factorizes VLA action generation into a trajectory anchor for coarse planning and residual refinement for local corrections, improving success rates by up to 7.8% in simulation and 18% on real robots across LIBERO, CALVIN, and physical tasks.
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SelfHeal: Empirical Fix Pattern Analysis and Bug Repair in LLM Agents
SelfHeal uses two ReAct agents and empirical fix patterns to repair bugs in LLM agents, outperforming baselines on a new 37-instance benchmark.
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UCCL-Zip: Lossless Compression Supercharged GPU Communication
UCCL-Zip adds lossless compression to GPU communication to reduce LLM bottlenecks while preserving exact numerical correctness.
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SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models
SIF creates semantically in-distribution fingerprints for LVLMs by distilling text watermarks into visual inputs and optimizing for robustness against detection and modification.
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Training Time Prediction for Mixed Precision-based Distributed Training
A precision-aware predictor for distributed training time achieves 9.8% MAPE across precision settings, compared to errors up to 147.85% when precision is ignored.
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FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
FineSteer decomposes inference-time steering into Subspace-guided Conditional Steering and Mixture-of-Steering-Experts to deliver stronger control over LLM behaviors with less utility loss than prior methods.
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How to Correctly Make Mistakes: A Framework for Constructing and Benchmarking Mistake Aware Egocentric Procedural Videos
PIE-V is a framework that injects plausible mistakes and corrections into egocentric procedural videos via psychology-informed planning and LLM-assisted video synthesis, paired with a nine-metric human rubric for benchmarking.
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Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching
Mixture-of-experts flow matching enables non-autoregressive language models to achieve autoregressive-level quality in three sampling steps, delivering up to 1000x faster inference than diffusion models.
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LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation
LWGR applies personalized soft instructions for LLM knowledge extraction and Lagrangian primal-dual optimization to selectively fuse beneficial world knowledge into generative recommendation while bounding degradation.
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Towards Design Compositing
GIST is a training-free identity-preserving image compositor that improves visual harmony when integrating disparate elements into design pipelines.
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SLQ: Bridging Modalities via Shared Latent Queries for Retrieval with Frozen MLLMs
SLQ adapts frozen MLLMs for multimodal retrieval by appending shared latent queries to text and image tokens and introduces KARR-Bench to test knowledge-aware reasoning retrieval.
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SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding
SpotSound adds a hallucination-suppressing objective and a needle-in-haystack benchmark to audio-language models, reaching state-of-the-art temporal grounding while keeping general task performance.
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Chain-of-Models Pre-Training: Rethinking Training Acceleration of Vision Foundation Models
CoM-PT trains vision foundation models in ascending size order using inverse knowledge transfer, allowing larger models to achieve superior performance with significantly reduced overall computational cost compared to individual training.
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MedVeriSeg: Teaching MLLM-Based Medical Segmentation Models to Verify Query Validity Without Extra Training
MedVeriSeg is a training-free framework that analyzes similarity maps from the [SEG] token and uses GPT-4o to verify whether a segmentation query targets an object actually present in a medical image.
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Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation
Hybrid Forcing combines linear temporal attention for long-range retention, block-sparse attention for efficiency, and decoupled distillation to achieve real-time unbounded 832x480 streaming video generation at 29.5 FPS.
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ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models
ASPIRin decouples speaking timing from token content via binary action space projection and applies GRPO with rule-based rewards to optimize interactivity in SLMs without semantic collapse or repetition.
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Single-Position Intervention Fails: Distributed Output Templates Drive In-Context Learning
ICL task identity is encoded as distributed output format templates across demonstration tokens rather than localized at single positions.
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BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning
BadSkill poisons embedded models in agent skills to achieve up to 99.5% attack success rate on triggered tasks with only 3% poison rate while preserving normal behavior on non-trigger inputs.
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Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.
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LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation
LAMP extracts continuous 3D inter-object transformations from image editing to serve as geometry-aware priors for zero-shot open-world robotic manipulation.
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Tool Retrieval Bridge: Aligning Vague Instructions with Retriever Preferences via Bridge Model
A bridge model rewrites vague instructions into specific ones, raising tool retrieval NDCG scores substantially on a new vague-instruction benchmark.
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Towards Knowledgeable Deep Research: Framework and Benchmark
The paper introduces the KDR task, HKA multi-agent framework, and KDR-Bench to enable LLM agents to integrate structured knowledge into deep research reports, with experiments showing outperformance over prior agents.
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SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)
The paper introduces the DimABSA shared task for SemEval-2026 that reformulates aspect-based sentiment analysis and stance detection as valence-arousal regression problems with subtasks for regression, triplet, and quadruplet extraction plus a new continuous F1 metric.
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Compression as an Adversarial Amplifier Through Decision Space Reduction
Compression acts as an adversarial amplifier by reducing the decision space of image classifiers, making attacks in compressed representations substantially more effective than pixel-space attacks under the same perturbation budget.
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REAgent: Requirement-Driven LLM Agents for Software Issue Resolution
REAgent improves LLM patch generation for software issues by 17.4% on average through automated construction, quality checking, and iterative refinement of structured issue-oriented requirements.
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Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
ChainFed achieves memory-efficient private LLM fine-tuning on edge devices through sequential layer-by-layer adapter training with dynamic co-tuning, perceptive optimization, and adaptive starting point selection, improving accuracy by up to 46.46%.
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The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
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EchoAgent: Towards Reliable Echocardiography Interpretation with "Eyes","Hands" and "Minds"
EchoAgent is a new agentic AI system that integrates visual observation, quantitative measurement, and expert knowledge reasoning to achieve reliable echocardiography interpretation with up to 80% accuracy on CAMUS and MIMIC-EchoQA datasets.
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One Model for All: Multi-Objective Controllable Language Models
Multi-Objective Control trains a single LLM as a preference-conditioned policy using multi-objective optimization in RLHF to produce outputs in user-specified regions of the Pareto front.
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TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models
TimelineReasoner applies large reasoning models in a Global Cognition plus Detail Exploration loop to produce more accurate, complete, and coherent timelines from news than prior LLM-based methods.
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Debiasing LLMs by Fine-tuning
Supervised fine-tuning with LoRA on rational benchmark forecasts corrects extrapolation bias out-of-sample in LLM predictions for controlled experiments and cross-sectional stock returns.
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EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment
EvaNet is a lightweight network that efficiently approximates image fusion metrics with improved consistency to human perception via decomposition, contrastive learning, and LLM input.
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An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
A scoping review of 12 studies finds LLM applications for rare disease patient education remain early-stage, dominated by general models like ChatGPT focused on curated question-answering with limited real-world or patient-centered evaluation.
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FASTER: Rethinking Real-Time Flow VLAs
FASTER adds a Horizon-Aware Schedule to flow VLAs that compresses immediate-action denoising to one step while keeping long-horizon trajectory quality, lowering real-robot reaction latency.
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Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation
Vaccine buyer's remorse occurs in under 2% of COVID-19 social media posts, mostly in skeptic communities via personal stories of health issues.
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ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors
ExpertGen generates high-success expert policies in simulation from imperfect priors by freezing a diffusion behavior model and optimizing its initial noise via RL, then distills them for real-robot deployment.
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Joint Optimization of Multi-agent Memory System
CoMAM jointly optimizes agents in multi-agent LLM memory systems via end-to-end RL and adaptive credit assignment to improve collaboration and performance.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
FlexServe achieves up to 10x faster time-to-first-token for secure LLM inference on mobile devices by using flexible resource isolation in TrustZone compared to standard approaches.