All papers

The authors introduce LIBERO-PeRM, a simulation benchmark for studying how embodied robotic agents learn and follow user preferences in manipulation tasks.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

The paper proposes an adaptive strategy using software agents and competency-based ontologies to assist students in e-learning platforms.

The paper introduces an asymmetric 2D tensor parallelism framework to optimize large language model inference across hardware with uneven GPU capacities.

The paper presents a task token framework designed to maintain sub-second runtime for multi-agent logistics operations with hundreds of agents.

The paper presents a spatio-temporal attention framework to predict trajectories of multiple mixed agents in unsignalized traffic roundabouts.

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The paper presents an autonomous framework using multiagent reinforcement learning to monitor, diagnose, and recover from failures in production AI systems.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

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The paper uses multi-agent language model simulations to evaluate why international news outlets replicate the agenda of major Western media.

The paper presents a multi-agent reinforcement learning framework for designing RNA sequences by co-optimizing both secondary topology and tertiary geometry.

The authors introduce DescribeEvent to show that language models can forecast temporal events when using natural-language descriptions and symbolic labels.

The paper introduces a token-level sparse attention method using a learned query-key subspace and an indexed kernel to accelerate long-context decoding.

The paper analyzes gendered reference replacements across science fiction and general fiction corpora using a masked language model.

The paper analyzes intermediate training checkpoints to trace when language models acquire morphological structures versus semantic embeddings during pretraining.

The paper examines using pre-trained language models without task-specific training for social science record linkage and demographic inference from names.

The paper shows that the Bradley-Terry model fails on Chatbot Arena for similar models, and introduces a parameter-efficient correction for ties.

The paper introduces a benchmark evaluating the robustness of typed decision models against text, image, and prompt injection attacks.

The paper introduces a benchmark and similarity metric to measure the algorithmic diversity of solutions produced by code generation models.

The paper presents a four-step methodological procedure to integrate ontology-guided coding and topic modeling representations of qualitative text data.

The paper proposes a regression-based meta-evaluation framework and benchmark to assess text evaluation metrics for multilingual style-personalized generation.

The paper investigates base language model benchmarking setups to identify evaluation configurations that produce consistent, non-random, and monotonic performance signals.

The paper presents a double-annotated sentiment dataset for Nepali and evaluates several open-source and commercial language models on it.

The paper introduces a training-free sampling framework that uses internal model activations to efficiently estimate rare failure probabilities in language models.

The paper proposes a parameter-efficient method leveraging frozen model representations for multi-output text regression tasks across diverse benchmarks.

The paper presents a framework to formalize compute, labor, and workflow costs as core measurement design criteria for automated essay scoring.

The paper presents an approach using character-level tokens, larger models, and synthetic data for the multilingual BabyLM data-efficiency challenge.

The paper introduces a benchmark to evaluate whether language models can accurately detect appropriate and inappropriate uses of persona information in dialogue.

The paper introduces a multi-stage topic modeling pipeline that merges fine-grained document clusters using a language model to balance efficiency and quality.

The paper proposes an experimental design that separates example selection from mass redistribution to analyze the effects of training-data quality filtering.

The paper shows that differences in predicted target tokens, such as whitespace, can falsely look like layer specialization across different corpora.

The paper proposes a multiple-summary metric and dataset to evaluate graded proposition salience across diverse text genres.

The paper introduces a reinforcement learning framework using intermediate-state branching and segment-level credit assignment for diffusion language models.

The paper studies how visual input signals affect scaling behavior and non-visual reasoning during native multimodal pretraining.

The paper analyzes post-training in audio-language models to separate genuine audio reasoning gains from question-format memorization.

The paper presents adapted small language models designed to directly score agricultural candidate choices in a single forward pass.

The paper develops a streaming end-to-end inverse text normalization architecture using a pretrained text-to-text model with a read-tag-write policy.

The paper proposes a decoding algorithm that denoises consecutive blocks concurrently by promoting sampler states in diffusion language models.

The paper analyzes reward hacking in verifiable instruction following and introduces a likelihood bonus method to maintain answer quality.

The paper presents an item response theory framework to jointly evaluate student performance on standard questions and tutoring dialogue subtasks.

The paper introduces a threat modeling framework examining adversarial query demotion and graph edit attacks on graph-based RAG systems.

The paper presents a method using an external encoder to induce latent soft-token reasoning on frozen language model backbones.

The paper introduces a multi-agent framework that reframes harmful requests using Moral Foundations Theory to test model jailbreak vulnerabilities.

The paper proposes a prefix-augmented distillation method providing one-step student branch feedback during language model pretraining.

The paper proposes a pair-norm guided mixed-precision quantization method for key caches during long-output transformer inference.

The paper reports a pre-registered test measuring the stability of history-based forecasts for model brand mentions over twenty days.

The paper identifies evaluation pitfalls in multimodal machine unlearning and introduces a benchmark grounded in real-world knowledge.

The paper proposes an interface for typed decision models that selectively invokes and interrupts reasoning to balance accuracy and latency.

The paper introduces a soft margin preservation target for supervised fine-tuning to retain non-target token rankings.

The paper introduces a 1,000-problem benchmark for constraint-based planning and decision-making in the Bangla language.

The paper analyzes retrieval failures in Mamba architectures under query-first settings and explores token representations and selection gates.

The paper presents a formal spatiotemporal framework to specify and verify road vehicle safety rules across continuous trajectories.

The paper proposes a relation-semantic reasoning framework to resolve disconnects between answer reachability and valid paths in biomedical graphs.

The paper presents a four-stage training pipeline combining preference optimization and distillation for instruction-following machine translation.

The paper reveals vulnerabilities in prompt-predictability metrics used to estimate human contribution in AI-assisted writing.

The paper introduces a speculative decoding system combining self-refinement and pipelined drafting to reduce wall-clock generation time.