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A study interviewed four AI models on 24 subjects, recording 1,452 positions to archive their explicit views when pushed for consistency.
The article likely presents research on contrastive language models, exploring the use of contrastive learning techniques in language model development.
FWBench introduces a benchmark for evaluating how language models select and use time-series forecasts to make cost-constrained decisions, comparing hosted and local configurations on electricity and cycle-hire datasets with efficient budget usage by GPT-6 Astra.
SPAR is a pretraining objective that uses a gated KL loss to stabilize language model predictions against irrelevant prefix text, improving robustness in long-context scenarios as demonstrated on multiple benchmarks.
This paper compares JEV with nine language models on ContractNLI, evaluating inference cost, response time, and correctness across various request configurations, finding that JEV has lower cost and response time while language models achieve higher baseline accuracy.
StateComp is a framework that compresses historical interactions in long-horizon agents based on the current state, reducing token usage by 52.27% and achieving a 12.67× speedup in representation extraction while maintaining task performance.
This paper studies hidden states in long horizon language model agents, revealing that memory compression and recall needs are encoded before actions. It proposes the PaMER framework to reduce context consumption while maintaining task performance through state-guided compression and evidence retrieval.
This paper uses activation patching to causally measure if reasoning steps in chain-of-thought are load-bearing, finding that behavioral tests overestimate faithfulness and larger models like Qwen3-4B maintain better faithfulness across reasoning depths.
This paper introduces Reinforcement Learning with Decomposed Subtasks (RLDS), a method that decomposes trajectory reward into per-subtask advantages to improve credit assignment in reinforcement learning for language model agents, showing significant gains on high-heterogeneity agentic benchmarks.
The paper introduces Modular Norm RandOpt, an architecture-aware perturbation method for efficient ensembling of language models, showing improved performance with fewer candidates across multiple tasks and model scales.
This paper studies how traits can persist across multiple generations of language models in training lineages, finding that traits may remain internally present even when behaviorally absent, with implications for model safety and training.
This paper distinguishes between aligning AI with human preferences versus human behavior, showing that preference alignment can reduce human-likeness and establishing a Turing-test gap in current alignment methods.
The paper introduces and evaluates a behavior layer for conversational agents that enables expressed doubt, provenance-aware assertions, and belief revision to improve truthfulness in AI systems.
MemoryAthena introduces a memory interface with three pathways—direct retrieval, generation from cues, and generation from backbone states—and learns to route interventions, improving performance on question answering and general NLP tasks.
This paper investigates graph-guided evidence navigation to improve long-novel reasoning with a local 9B language model, providing exploratory evidence from experiments on detective novels without statistically significant gains over baselines.
ISA-Bench introduces a benchmark using programming games with constrained instruction sets to evaluate computational reasoning in large language models, revealing insights into model capabilities and a reasoning-execution gap.
The paper introduces Matryoshka Attribution (MAttr), a mask-learning method for attributing language model outputs to internal components, achieving top performance on the Mechanistic Interpretability Benchmark and demonstrating practical use in modifying LLM behaviors by adjusting weights.
The paper studies how 60 language models from 13 vendors behave when users apply pressure, finding that folding rates correlate with model recency while holding manner varies by vendor. It also shows LLM coders can consistently apply human-authored codebooks, indicating humans should focus on defining behaviors rather than labeling volume.
The paper introduces LatentPort, a method for transferring persistent recurrent state from a 4B to a 9B hybrid language model without the receiver re-reading context, demonstrating improved performance with minimal correction and near-native continuation.
This paper evaluates numerical representation invariance in language models, finding that evaluator interface issues can mimic reasoning failures and identifying model-specific errors like unit conversion problems in Mistral Small 4.