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small language model

3,244 papers

#machine learning Preprint Sep 2026

The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally

Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is often tuned per model. We study where quantization damage occurs and how to allocate a small additional precision budget. Using causal mixed-precision intervention as ground...

Jun-Hao Hu, S. Ramachandran · 1 citation
#machine learning Preprint Sep 2026

CRAFT: Fine-Tuning Pre-hoc Explainability in AI-native 6G RAN

CRAFT, which stands for Cold-start Reasoning Alignment Alignment via Fine-Tuning, is proposed, a data-centric method to autonomously generate a verified dataset of (input, trace, label) triplets, requiring substantially less compute and wall-clock time than GRPO-based methods.

Pranshav Gajjar, V. K. Shah · 0 citations
#artificial intelligence Preprint Sep 2026

CordisBench: Can Language Models Reason About Component Lifecycles in Dynamic Agent Harnesses?

CrisBench, a 1,200-question benchmark of lifecycle reasoning that combines a controlled formal setting with programs executed against Cordis, a runtime that manages component dependencies and cleanup, and asks models to identify affected components, predict state after a specified teardown order, and determine which co...

Damien Sileo, Dimitri Kachler · 1 citation
#artificial intelligence Preprint Sep 2026

From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification

A framework is proposed that identifies which label pairs the model struggles to distinguish, expands the candidate set to include confusable labels, and generates targeted rules to differentiate between similar candidates, which requires no fine-tuning and transfers to smaller, cheaper models.

Manish Gupta, Chaitanya Giri, Jayasimha Talur · 0 citations
#artificial intelligence Preprint Sep 2026

LatentPress: Context Compression Beyond Text and Vision

LatentPress is introduced, which writes conversational histories and long documents into a third representation: continuous memory tokens that a frozen decoder reads directly through its input-embedding interface, with no text reconstruction at inference.

Yuanda Xu, Zheng-Ze Zhou, He-Jian Sang et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search

This work introduces Power-Law Entropy Search (PLES), a computational cost-aware acquisition function built on multi-fidelity Bayesian optimization that efficiently estimates optimal hyperparameter scaling laws through adaptive experimentation using less than one-tenth of the computational budget required by convention...

Zhiliang Chen, S. Ament, David Eriksson et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching

DronesCATS-Agent, an architecture where the MLLM is a swappable component, and DroneCATS, a benchmark treating the model as the independent variable are introduced, an architecture where the MLLM is a swappable component and DroneCATS, a benchmark treating the model as the independent variable.

Jaewoo Park, Minyoung Lee, Sukmin Seo et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Restrict, Don't Retrain: Inference-Time VLM Guidance for Zero-Shot Aerial Segmentation

A single consumer-grade GPU running a vision-language model (VLM) is used to supply missing guidance on pixel-level segmentation, improving segmentation while producing structured, auditable evidence that drives the result and can be inspected on its own.

Teresa DiMeola, Charles Walter, Hong Xiao · 0 citations
#artificial intelligence Preprint Aug 2026

HBQ: Hierarchical Scaling Block Quantization with Hardware-Efficiency-Aware Design for Accurate LLM Inference

Hierarchical Block Quantization (HBQ) uses large blocks to maximize efficiency and introduces low-overhead significand (SIG) scaling for second-level quantization and compensates for large-block errors more effectively than prior PoT and INT schemes.

Chun-Ting Chen, Dongguang Han, Han-Gyeol Mun et al. · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 15, 2026

AI model decodes the language cells use to communicate

A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.

Microsoft Research Blog Oct 15, 2026

Proof-based general-purpose computing with Magnetar

Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.

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