EnergyAgentBench is a new benchmark with 70 task variants that evaluates LLM agents on live energy data for datacenter siting, long-horizon optimization, and causal grid diagnosis.
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HuggingFace's Transformers: State-of-the-art Natural Language Processing
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abstract
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. \textit{Transformers} is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. \textit{Transformers} is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at \url{https://github.com/huggingface/transformers}.
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- abstract Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. \textit{Transformers} is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrain
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representative citing papers
Sieve dynamically schedules MoE experts across GPU and PIM hardware to handle bimodal token distributions, achieving 1.3x to 1.6x gains in throughput and interactivity over static prior PIM systems on three large models.
VibeServe demonstrates that AI agents can synthesize bespoke LLM serving systems end-to-end, remaining competitive with vLLM in standard settings while outperforming it in six non-standard scenarios involving unusual models, workloads, or hardware.
TTT layers treat the hidden state as a trainable model updated at test time, allowing linear-complexity sequence models to scale perplexity reduction with context length unlike Mamba.
RULER shows most long-context LMs drop sharply in performance on complex tasks as length and difficulty increase, with only half maintaining results at 32K tokens.
Pythia releases 16 identically trained LLMs with full checkpoints and data tools to study training dynamics, scaling, memorization, and bias in language models.
Task vectors from weight differences allow arithmetic operations to edit pre-trained models, improving multiple tasks simultaneously and enabling analogical inference on unseen tasks.
An unsupervised technique extracts latent yes-no knowledge from language model activations by locating a direction that satisfies logical consistency properties, outperforming zero-shot accuracy by 4% on average across models and datasets.
Polar is a new cross-context benchmark showing LLM political bias measurements are not fixed but vary with country, issue, model, and language.
Fine-tuned Mistral-7B via QLoRA achieves up to 12% higher F1 than GPT-4o on biomedical claim verification with 1008 examples, identifies a structural shortcut in SciFact, and shows robust cross-domain transfer from sound data.
M* introduces the Walk Graph abstraction to serve arbitrary compositions of multimodal model components and reports latency and throughput gains over vLLM-Omni and other baselines on text-to-image, text-to-speech, and robotic planning workloads.
A unifying framework decomposes concept alignment into instance-wise and distributional translation and concept consistency, introduces the InterVenchA benchmark, and shows that joint optimization via CoSAE recovers strong alignment even with 0.1% paired data.
Iterative solvers in layer-wise model merging act as spectral regularizers on an ill-posed interference operator; closed-form SWUDI and adaptive SWUDI-A match or exceed SOTA merging accuracy with 28-72x wall-clock speedup.
A new fault-injection framework enables a systematic empirical study that produces 17 takeaways on error propagation in LLM inference and four software-only mitigation directions.
Test-time training enables three new threat models that raise jailbreak attack success rates on language models to averages of 95% and 93% ASR@10 under LoRA for few-shot and generation-phase attacks across model families.
Video-LLMs exhibit directional motion blindness from a direction binding gap; DeltaDirect projector objective lifts synthetic accuracy to 85.4% and real accuracy by 21.9 points while preserving other video capabilities.
Introduces interference-aware multi-task unlearning with task-aware gradient projection and instance-level gradient orthogonalization, reducing interference scores by 30.3% and 52.9% on vision benchmarks.
TokAlign++ learns token alignments between LLM vocabularies from monolingual representations to enable faster adaptation, better text compression, and effective token-level distillation across 15 languages with minimal steps.
EdgeFlowerTune is a real-device benchmark that jointly assesses model quality and system costs for federated LLM fine-tuning on edge hardware using three protocols: Quality-under-Budget, Cost-to-Target, and Robustness.
DAPRO provides the first dynamic, theoretically guaranteed way to allocate interaction budgets across test cases for bounding time-to-event in multi-turn LLM evaluations, achieving tighter coverage than static conformal survival methods.
Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
Auto-FlexSwitch achieves efficient dynamic model merging by decomposing task vectors into sparse masks, signs, and scalars, then making the compression learnable via gating and adaptive bit selection with KNN-based retrieval.
SecureRouter accelerates secure transformer inference by 1.95x via an encrypted router that selects input-adaptive models from an MPC-optimized pool with negligible accuracy loss.
Agreeableness in AI personas reliably predicts sycophantic behavior in 9 of 13 tested language models.
citing papers explorer
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EinSort: Sorting is All We Need for Tensorizing LLM
Sorting tensor indices enables an adaptive tensorization method that discovers low-rank structure in LLM weights and KV caches, yielding better reconstruction quality than baselines.
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Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models
SAGE-PTQ is a graph-guided ultra-low-bit PTQ framework that achieves 1.03 average weight bits and 0.004 scaling bits per matrix on LLMs while reporting lower perplexity and memory use than BiLLM and PB-LLM.
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Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt
Larger LLMs reproduce constructional productivity via entrenchment in coercion cases with nonce words but fail to use statistical preemption to avoid overgeneralizing semantically plausible but unobserved patterns.
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Mapping Whisper Representations to Human ECoG Responses with Interpretable Time-Resolved Neural Encoding
The paper introduces a time-resolved neural encoder combining Whisper embeddings with recurrent temporal modeling and soft attention to predict ECoG responses, finding strongest alignment in intermediate layers and anatomically coherent phoneme organization in electrodes.
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DAG-MoE: From Simple Mixture to Structural Aggregation in Mixture-of-Experts
DAG-MoE uses a lightweight module to learn DAG-based structural aggregation of selected experts, expanding combination space and enabling intra-layer multi-step reasoning compared to standard weighted-sum MoE.
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QVGGT: Post-Training Quantized Visual Geometry Grounded Transformer
QVGGT uses per-block mixed-precision analysis, outlier token filtering with PCA compensation, and task-aware scale search to achieve near-lossless W4A16 quantization of VGGT with 3-4.9x memory savings and 2.8x speedup.
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SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
SemStruct models tables as heterogeneous graphs with GNNs on frozen PLM embeddings to incorporate row co-occurrences for schema matching and reports SOTA results on Valentine and SOTAB-SM benchmarks.
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Personalized Generative Models for Contextual Debiasing
DecoupleGen personalizes diffusion models to create images with uncommon contexts for debiasing object recognition, yielding consistent gains on scene classification tasks.
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Latency Analysis and Optimization of Alpamayo 1 via Efficient Trajectory Generation
Redesigning Alpamayo 1 to single-reasoning and optimizing diffusion action generation cuts inference latency by 69.23% while preserving trajectory diversity and prediction quality.
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Reasoning Compression with Mixed-Policy Distillation
Mixed-Policy Distillation transfers concise reasoning behavior from larger to smaller LLMs by having the teacher compress student-generated trajectories, cutting token usage up to 27% while raising benchmark scores.
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EGAD: Entropy-Guided Adaptive Distillation for Token-Level Knowledge Transfer
EGAD adaptively distills LLM knowledge at the token level by using entropy to create a curriculum from low- to high-entropy tokens, adjust temperature, and switch between logits-only and feature-based branches.
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GiVA: Gradient-Informed Bases for Vector-Based Adaptation
GiVA uses gradients to initialize vector adapters so they match LoRA performance at eight times lower rank while keeping extreme parameter efficiency.
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Towards Better Static Code Analysis Reports: Sentence Transformer-based Filtering of Non-Actionable Alerts
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
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Reconstruction of a 3D wireframe from a single line drawing via generative depth estimation
A latent diffusion model conditioned on line drawings estimates dense depth to reconstruct 3D wireframes, reporting 5.3% average depth error after training on over one million pairs.
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FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs
FedSpy-LLM uses gradient decomposition and iterative alignment to reconstruct larger batches and longer sequences of training data from LLM gradients in federated settings, including with PEFT methods.
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Ranking Reasoning LLMs under Test-Time Scaling
Many established statistical ranking techniques produce orderings of reasoning LLMs under test-time scaling that closely match a Bayesian gold standard, with mean Kendall tau_b of 0.93-0.95 at full trials and best methods reaching 0.86 at single trials.
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A Simple Method to Enhance Pre-trained Language Models with Speech Tokens for Classification
Lasso-selected speech tokens enhance text LLMs for multimodal classification by reducing long audio sequences to task-relevant features via self-supervised adaptation.
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Automated Annotation of Shearographic Measurements Enabling Weakly Supervised Defect Detection
An automated annotation pipeline combining Grounded DINO and SAM produces usable bounding boxes and masks for weakly supervised defect detection in shearography.
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Different types of syntactic agreement recruit the same units within large language models
Different types of syntactic agreement recruit overlapping units within LLMs, indicating that agreement forms a meaningful functional category across English, Russian, Chinese, and structurally similar languages.
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Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models
Genome-Factory is an open-source Python library that integrates data pipelines, model tuning, inference, benchmarks, and biological interpretation for genomic foundation models.
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Shared representations in brains and models reveal a two-route cortical organization during scene perception
RSA on 7T fMRI during natural scene viewing identifies ventromedial and lateral occipitotemporal representational routes for scene context versus animate content, with differential alignment to vision and language models.
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RAP: Runtime Adaptive Pruning for LLM Inference
RAP is a reinforcement learning framework for runtime-adaptive pruning of LLMs that jointly optimizes model weights and KV-cache usage under varying memory budgets.
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Diffusion Models are Secretly Zero-Shot 3DGS Harmonizers
D3DR optimizes inserted 3DGS objects with a DDS-inspired diffusion objective plus a new personalization step to match scene lighting, reporting 2 dB PSNR gain over prior methods.
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The Platonic Representation Hypothesis
Representations learned by large AI models are converging toward a shared statistical model of reality.
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Accelerating Reproducible Research in Synthetic EHR Generation
A new end-to-end benchmarking framework unifies synthetic EHR generators for longitudinal ICD codes with standardized training and architecture-agnostic evaluation including bootstrapped confidence intervals.
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KliniskVestBERT: BERT Model Specialised to Norwegian Clinical Texts
Three BERT models are further pre-trained on Norwegian clinical notes and discharge summaries, then shown to outperform their base models on synthetic clinical benchmarks and real-world tasks.
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OpenSOC-AI: Democratizing Security Operations with Parameter Efficient LLM Log Analysis
LoRA fine-tuning of TinyLlama-1.1B on 450 SOC examples produces 68% threat classification accuracy and 58% severity accuracy on 50 held-out logs, with full code, weights, and data released.
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Are vision-language models ready to zero-shot replace supervised classification models in agriculture?
Zero-shot VLMs reach at most 62% accuracy on agricultural classification tasks while supervised models like YOLO11 perform markedly higher, indicating they are not ready to replace task-specific systems.
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LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers
PreScope combines a layer-aware activation predictor, cross-layer prefetch scheduling, and asynchronous I/O to deliver 141% higher throughput and 74.6% lower latency for MoE inference on legacy hardware.
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Adapting Vision-Language Models for Neutrino Event Classification in High-Energy Physics
Fine-tuned LLaMA 3.2 VLM outperforms CNN baselines on neutrino event classification while adding interpretability via language reasoning.
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Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.
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Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation
CausalGAN + SAC RL pipeline generates synthetic bond yield data; fine-tuned Qwen2.5-7B LLM produces trading signals, with reported MAE 0.103, 60% profit rate, and LLM score 3.37/5.
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