The authors introduce MedEvoEval, an executable longitudinal evaluation framework designed to assess the continual evolution of doctor agents through simulated outpatient clinical episodes. This system moves beyond static benchmarks by tracking how agents acquire evidence, utilize resources, and refine their decision-making across multiple interactions.
The authors introduce GRAB, a constructor-encoder-bridge pipeline designed for table question answering that lifts relational data into a heterogeneous graph and encodes it via message passing. The method transfers signals to a frozen large language model through a small set of query-conditioned latent tokens, providing a compact structural representation while preserving the LLM's general reasoning capabilities.
Researchers introduce FinInvest-GTCN, a Graph-Temporal-Causal Network designed to optimize venture capital investment decisions by addressing challenges like heterogeneous data and non-stationary time series. The model redefines the task from content recommendation to quantitative risk-return assessment, utilizing a relational graph encoder, multi-scale temporal fusion, and a causal decision head to generate interpretable predictions.
The authors introduce the Electro-Visual-Language Assistant (EVLA), a framework that integrates multi-modal scene understanding with real-time perception of an electrified powertrain's electro-mechanical state to improve driving decisions. This approach addresses the limitation of existing vision-language models that treat vehicle dynamics as a black box by incorporating physical constraints and optimization objectives.
The A3M framework addresses the challenges of learning to bid in repeated multi-unit auctions by integrating adaptive deep reinforcement learning, adversarial reasoning, and multi-objective reward design. It utilizes an actor-critic backbone and opponent modeling to optimize strategy against non-stationary adversaries while balancing utility, revenue, and fairness.
This paper proposes a filtering defense against dirty-label poisoning attacks on speech commands classification systems by clustering unsupervised representations to identify and remove poisoned training data.
This study investigates whether large language models can recover the statistical characteristics of a broader population using only a small pilot sample of human responses. The authors decompose this recovery into three axes: structural fidelity, marginal fidelity, and individual fidelity.
An audit of fourteen mainstream large language models reveals a significant shift in racial bias within resume screening algorithms over recent years. While 2023-vintage models reproduce pro-White callback gaps, all models released in 2024 or later show either null gaps or significant pro-Black reversals.
The paper introduces AgriTune-R, a reproducible and auditable framework designed to adapt general-purpose large language models for specific agricultural applications. This approach addresses the domain-specific, safety-critical nature of agriculture by integrating data governance, expert evaluation, and evidence constraints to prevent unreliable advice.
This article introduces BERTomelo, a next-generation monolingual encoder specifically optimized for the Portuguese language using the ModernBERT architecture.
The authors adapt the open-source IndicTrans2-1B translation system to handle conversational register across 21 Indic languages using only public datasets. By combining experience replay with model souping, they achieve significant improvements in automatic metrics without degrading performance on general domain tasks.
A study of 22 open-weight large language models reveals that while the strength of clinical evidence can be recovered from model activations and text, the grades explicitly stated by the models are no better than chance. Researchers analyzed 45,134 clinical claims harmonized into four-level evidence grades to test whether models register and express evidence strength distinct from factual truth.
Researchers investigate the distributional gap between synthetic and real speech in LLM-based automatic speech recognition (ASR) systems by probing a SLAM-ASR architecture. They identify that discriminative signals separating the two data types are concentrated in the early-to-middle layers of the model backbone.
This paper introduces a continuous decoding framework for masked diffusion language models (MDLMs) that reinterprets mask prediction as clean-state prediction to induce a continuous flow in input embedding space. By allowing tokens to accumulate partial progress and remain revisable, the method addresses the premature commitments inherent in standard binary unmasking regimes.
ThinkProbe is a framework for the structural analysis of large language model reasoning traces, converting them into directed Thought Graphs with eight node types and six edge types. It derives a 19-metric five-dimensional cognitive profile through a fully non-generative pipeline combining rule-based segmentation and discriminative semantic linking.
This study investigates the extent to which modern text encoders capture psychological theories of affect by evaluating twelve recently released models across three established emotion frameworks. The research compares word-level and sentence-level performance using both regression and classification tasks.
This study evaluates whether mid-scale Multimodal Large Language Models (MLLMs) can perform localized concept naming under strict zero-shot conditions by assigning labels to bounding-box regions. The authors propose a reproducible evaluation protocol for Concept Naming that includes closed-set prompting and an embedding-similarity-based strategy for large label spaces.
Researchers introduce Evolution Fine-Tuning (EFT), a mid-training paradigm that teaches Large Language Models to evolve solutions across diverse tasks by converting evolutionary search trajectories into supervision. This approach addresses the limitation of prior methods that discard accumulated experience, enabling models to reuse discovery capabilities rather than solving new problems from scratch.
AB-RAG is a training-free, backbone-agnostic framework that dynamically adjusts retrieval efforts based on a confidence estimate derived from model certainty, answer-evidence agreement, and retrieval score variance. This approach allows systems to decide whether to stop or retrieve more evidence within a fixed budget without retraining the underlying language model.
This study investigates whether language models recognize when they are being tested, a factor critical for AI safety as it may cause models to alter their behavior strategically. Using 11 open-weight models from the Qwen 2.5, Gemma 2, and Llama 3.2 families, researchers analyzed how evaluation awareness manifests across different model sizes.