All papers

This paper proposes a low-rank bypass to accumulate small updates and reduce compounding errors in quantized recurrent model states.

NepPlural provides an annotated benchmark of Nepali discourse to evaluate how reliably language models capture diverse socio-cultural personas.

This paper studies multi-domain training setups to improve zero-shot sentiment classification transfer across unseen domains in Marathi.

The authors evaluate domain-adaptive pretraining and back-translation data augmentation to improve sentiment classification in Marathi.

RSIPO introduces a reinforcement learning framework where agentic skills and policy models co-evolve using self-extracted trajectory experience.

This work analyzes whether policy capability gaps or judge models dictate reward hacking when training models against language-model judges.

Heterogeneous Structured Pruning selects sublayer-specific sparsity patterns guided by latency and memory models for hardware-efficient inference.

This study analyzes how category-based property induction emerges in language models during pretraining using a synthetic hierarchical taxonomy.

The study tests whether neural models bind phoneme identity to serial position using state direction and magnitude representations.

This paper compares observational feature discovery scores in sparse autoencoders against actual causal intervention effects on model outputs.

This work evaluates machine-text detectors on Kazakh text using pre-ChatGPT human writing and shows significant false-positive rates.

RIVO is a lightweight plug-in framework that adaptively reroutes research agent trajectories to prevent premature stopping or wasted steps.

MedDEx combines language models for patient data extraction with deterministic rule engines to execute clinical treatment guidelines accurately and efficiently.

The paper develops a natural language processing workflow to check reading list alignment with curriculum objectives and topic balance.

This paper investigates batch size, regularization methods, and runtime tradeoffs when pretraining language models on repeated finite data.

The authors examine failure points when projecting complex emotion dataset labels onto single positive or negative valence polarities.

PAIR splits context processing and answer reasoning between two different-sized models, fusing predictions at the logit level for efficient inference.

This paper measures whether ecological alignment in large language models survives under pressure using a calibrated pretraining ladder.

ScaleMAS coordinates multi-agent systems using an asynchronous shared datastore with limited search to improve scaling and fault tolerance.

This paper presents Hashrope, a unified persistent data structure supporting incremental edits, branching, and cache reuse for language model serving.

This paper presents CacheFuse, a training-free technique enabling heterogeneous language models to communicate directly by sharing key-value cache states.

This paper proposes DeepThink, a conceptual architecture that decouples knowledge capacity, activation width, and recurrent depth in language models.

This paper introduces Lüshi-1840, a pre-modern Chinese language model trained on historical texts to evaluate knowledge cutoffs and hindsight bias.

This paper analyzes prediction-derived error updates and test-time gradient objectives in trained test-time-training layers.

This paper demonstrates that deep speech representations track word timing along low-dimensional circular trajectories across multiple model architectures.

This paper introduces RelLedger, an auditing framework to trace how relational histories drive reporting choices and actions in multi-agent systems.

This paper presents ATTCK-R, a planning-driven multi-turn red teaming framework to evaluate language models guarded by prompt safety defenses.

This paper proposes TAPS, combining standard accuracy training with post-hoc confidence selection to give controllable abstention rates without specialized tuning.

This paper proposes RIFT, a method using electrical resistance analogies to trace token influence through transformer attention graphs.

This paper introduces LoongSage, an asynchronous reinforcement learning framework designed to run multi-turn agent tasks with reduced system overhead.

This paper proposes verbal regularization prompts for on-policy self-distillation to prevent teacher models from taking shortcut reasoning paths.

This paper introduces HexaBee-4B, a translation model trained across six languages using joint multilingual answering and bidirectional supervision.

This paper uses activation steering and probing to identify distinct internal representations for rule-based versus consequence-based moral reasoning in language models.

This paper presents PersonaFactors, a framework that distills reusable preference patterns across tasks for dynamic prompt personalization in language models.

This paper introduces a multi-agent sandbox benchmark to evaluate language models on state inference, forecasting, and intervention prediction.

This paper presents a label differential privacy framework to protect survey-derived ordinal preference data during language model alignment.

This paper presents a case study of an interpreter co-creating a local computer-assisted interpreting tool using large language models.

This position paper argues that large language models perform language as social practices rather than building distributed representations of symbols.

This paper introduces a training-free decoding rule that anchors multi-token format units to ensure parseable parallel generation in diffusion language models.

This paper examines neuron relevance characterization methods based on magnitude and distribution support for selecting instruction fine-tuning data.

This paper investigates gradient spectral properties during reinforcement learning with verifiable rewards to design an adaptive-rank optimizer for reasoning models.

This paper presents a self-supervised recursive hypernetwork framework using parse trees to learn syntactically informed compositional sentence representations.

This paper introduces an evaluation index to measure whether machine translation of emergency communication preserves critical safety actionability.

This paper introduces a Chinese multi-task evaluation benchmark covering six recruitment application areas for language models.

This paper evaluates whether attention-graph curvature features can help verifiers detect errors in mathematical reasoning steps.

This paper investigates internal model representations that separate assistant actions from externally provided text across conversational layers.

This paper introduces an environment benchmark and exploration framework to help graphical user interface agents learn unfamiliar interaction dynamics.

This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses.

This paper investigates output diversity and text homogenization across twenty-three open-weight language models and thirteen languages.

This paper presents a psychometric framework to test whether automated sentiment classification remains equivalent across different social media platforms.

This paper constructs joint token probability laws from physics models to improve speculative verification in parallel language model decoding.

This paper proposes a cumulative value parameterization method for reinforcement learning to improve credit assignment in multi-step language reasoning.

This paper uses Bell's theorem bounds to analyze capacity limits and task interference in multi-task shared representations.

This paper analyzes label collapse failure modes across diffusion and autoregressive language models during in-context learning classification.

This paper introduces a repository-grounded benchmark to evaluate how well coding agents follow engineering constraints and developer workflows.

This paper introduces a benchmark to test if coding agents can build reliable validation test harnesses from natural language specifications.

This paper develops an uncertainty estimation framework for omnimodal models using external embeddings to score reasoning grounding and coherence.

This paper tests how language models handle explicit instructions when subsequent contextual sentences provide conflicting word definitions.

This paper presents a language model framework to extract event representations from autobiographical narratives into geometric state spaces.

This paper uses survival analysis on Reddit text and activity to predict when users will seek mental health support.