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Same Rule, Different Verdicts: Rubric Language and Clause Structure in LLM Judges for Korean Support Dialogues
Services that operate in Korean often write their evaluation rubrics in Korean and hand them to an LLM judge.When Strong Reasoners Join the Pool: Small Union Premiums and Costly Runaway Generations in LLM Routing
Routing benchmarks often judge a router against a hindsight oracle that counts a query as solved whenever any model's recorded answer is correct, but under stochastic decoding that oracle mixes two…Evaluation Contracts and Evaluation Leverage: Sliced-Gap Readings in Structured Prediction Can Move Without Model Change
NLP evaluation reports routinely cite sliced performance differences, most prominently the intra-inter gap in document-level relation extraction, as capability evidence.Say Less, Mean Better: Training-Free Verbosity Control for Language Models
Large language models (LLMs) often generate responses longer than necessary, increasing inference latency and computational cost.LoopKV: Loop-Aware KV Cache Compression for Serving Looped Transformers
Looped transformers apply a shared block several times per token to improve accuracy without adding parameters.Where Do Distilled Students Store Thinking?
Small students degrade on large reasoning models' (LRMs') long solutions, so recent work shortens them by interpolating an LRM with a short-answer model at ratio $λ$.AdSteer: Native Advertising in Large Language Model Generation
Large language model (LLM) services face rapidly rising serving costs.PACT: Certifying What Is Written in Parallel Decoding of Diffusion Language Models
Parallel decoding speeds up masked diffusion language models by writing several tokens per forward pass.Length Is Cheap, Competition Is Not: Attention Predicts Context Rot in Retrieval
Why do language models fail on long inputs?Tokenizer Myths and Where To Find Them
Tokenization is a fundamental component of modern language models: before a model sees data, its tokenizer has already determined how the data appears.Shared Context Candidate Scoring with Jointly Adapted Readout Networks
Repeated decisions over a shared document create two distinct opportunities: reusing document computation and adapting how question representations score candidate answers.Constructing a Regional Slang Lexicon for Electricity Demands via Soft-Label Bidirectional Anchoring and Active Learning
Power-grid customer service increasingly relies on community social media, where customers describe outages, billing, and equipment faults in informal regional dialect.COMPREHEND: Ultra-Fine-Grained Benchmark Understanding by Entailment Hierarchy
Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories is crucial for LLM practitioners.Neologism Inoculation Prompting
Inoculation prompting is a method to reduce undesired behaviours such as reward hacking in frontier models.Coarse Semantic Blocks for Efficient Training-Free Sparse Attention
We introduce a training-free sparse attention method that builds a prompt-specific key index to accelerate both prefill and decoding.Text-to-SQL Models Make Choices Without Saying So
A text-to-SQL answer can run, return a plausible number and still compute something other than what the analyst meant.Sparse Internal Control of Language Models
Language models are increasingly used in text generation, decision support, and automated interaction, creating a need for localized, reversible, and selective control of their behavior.Do Multi-Turn Conversational Capabilities Carry Over from Text to Speech? A Parallel Multilingual Benchmark
Successful multi-turn voice interaction requires models to retain information from earlier turns, follow persistent instructions, and track revisions to conversational state.Voluntary Servitude: Weaponized Safety Warning Mechanisms for Controlling AI Agents
LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a transaction, a dependency, or a decision with…The Min-Max theory of language behavior
The "Min-Max" theory of language behavior proposes that humans engage simultaneously in minimization of processing effort and maximization of information content, and that this is a primary driver…