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.
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.
VertAX supplies a differentiable JAX implementation of vertex models for confluent epithelia that enables forward simulation, mechanical parameter inference, and inverse design of tissue-scale behaviors.
GhostServe applies erasure coding to KV cache in host memory for fast recovery from failures in LLM serving, cutting checkpointing latency up to 2.7x and recovery latency 2.1x versus prior methods.
Variability modeling from software engineering enables systematic sampling, measurement, and prediction of LLM inference configurations for energy, latency, and accuracy trade-offs.
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BAMI: Training-Free Bias Mitigation in GUI Grounding
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Scaling Pretrained Representations Enables Label-Free Out-of-Distribution Detection Without Fine-Tuning
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On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
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When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
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Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study
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R-CoV: Region-Aware Chain-of-Verification for Alleviating Object Hallucinations in LVLMs
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RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
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Causal Drawbridges: Characterizing Gradient Blocking of Syntactic Islands in Transformer LMs
Causal interventions reveal that coordination islands block filler-gap mechanisms in Transformers in a gradient way matching humans, yielding the hypothesis that 'and' encodes relational dependencies differently in extractable vs. conjunctive uses.
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SeLaR: Selective Latent Reasoning in Large Language Models
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Rethinking Residual Errors in Compensation-based LLM Quantization
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Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
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Jump Start or False Start? A Theoretical and Empirical Evaluation of LLM-initialized Bandits
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MemFactory: Unified Inference & Training Framework for Agent Memory
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Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
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Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models
LLMs dynamically construct and causally rely on structured conceptual subspaces in middle-to-late layers for in-context inference.
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Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
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TreeCoder: Systematic Exploration and Optimisation of Decoding and Constraints for LLM Code Generation
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Citation Failure: Definition, Analysis and Efficient Mitigation
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Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
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Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
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TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs
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Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction
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OpenVLA: An Open-Source Vision-Language-Action Model
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Steering Llama 2 via Contrastive Activation Addition
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Zephyr: Direct Distillation of LM Alignment
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DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
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BlockPilot: Instance-Adaptive Policy Learning for Diffusion-based Speculative Decoding
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On the Vulnerability of Parameter-Level Defenses to Model Merging
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From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model
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A Classroom Study of LLM-Generated Feedback Intervention in Introductory Programming
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EinSort: Sorting is All We Need for Tensorizing LLM
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Minimizing the Hidden Cost of Scales: Graph-Guided Ultra-Low-Bit Quantization for Large Language Models
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Linguistic Productivity in Large Language Models: Models Coerce, but do not Preempt
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