small language model

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.

Phantom Gains: Auditing Self-Improvement Against a Measured Null

Auditing three rounds of rank-$32$ LoRA self-training on Qwen3-8B against a frozen control pushed through the identical pipeline, this work identifies seven measurement failures, each of which inverts a reported finding when its control is absent.

SYNGAP1-related disorder: pathophysiology, epilepsy, cognitive and behavioral phenotypes, and precision therapeutic approaches.

A rapidly advancing precision-therapy pipeline-including antisense oligonucleotides to upregulate the intact allele, AAV-based gene replacement, CRISPR-mediated transcriptional activation, epigenetic modulators, and rational pathway-targeted small molecules-offers realistic prospects for disease modification.

Leveraging small language models for model predictive control of dynamic systems

Constitutional Midtraining: Content Presence Drives Alignment Gains

Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when cleanly isolated from post-training. We build a 394M-token constitutional corpus from Anthropic's Constitution and apply constitutional midtr...

Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry

Analysis of the pre-trained embedding geometry of a small sentence transformer (SBERT) and classic SLM reveals that pre-trained embedding geometry is associated with classification performance and reveals a counterintuitive finding that a structured input that would help a human reader does not improve the SLM performa...

Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents

This work formalizes the hybrid LLM-planner and RL-controller architecture as a Goal-Augmented Markov Decision Process and shows that when the LLM per-state progress score is used as a bounded potential function, the resulting shaping term preserves the optimal policy set even when the LLM scores are inaccurate.

Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis

This paper investigates how multilingual medical adaptation reshapes the internal representations of Whisper models through layer-wise encoder analysis, and shows that English medical fine-tuning produces the dominant encoder shift, whereas multilingual continuation largely preserves the adapted representation space.

Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication

Verifiable Latent Alignments (VLA), an activation-aware framework for monitoring and steering these private communication channels, is introduced and shows that the evaluated private channel attacks can be monitored without training the primary monitor on attack examples and mitigated when matched counterfactual access...

Breaking the weakest link to evade vision language models

To efficiently generate adversarial examples, a gradient-based attack method is proposed that performs optimization exclusively on the vision encoder of the VLM rather than on the entire multimodal architecture, which significantly reduces the computational cost and resource requirements of the attack while maintaining...

TestifAI: Tomography-Based Testing for Deep Learning Systems

TestifAI, a deep learning testing framework for efficient and accurate estimation of robustness against combinations of perturbations, is proposed and partial model tomography is introduced, a novel approach to reconstructing model behaviour in a multi-perturbation space from tests that apply only a small number of per...

Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions

SDDL is introduced, a neuro-symbolic framework that translates natural-language scheduling problems into compact, solver-aligned representations of tasks, resources, constraints, and objectives, while delegating low-level modeling and search to a deterministic compiler and external solver.