zero-shot learning

topic10 events

papersTODAY 04:00 UTC

DecompressionLM probes language models for concept graphs without preset queries

A new arXiv preprint introduces DecompressionLM, a stateless approach for extracting concept graphs from language models in a zero-shot setting. Unlike prior probing work that depends on predefined queries and can only surface concepts already known to researchers, this method aims to reveal structures the model has encoded on its own. The authors describe the framework as deterministic and diagnostic, and the paper is a revised submission.

interpretability/zero-shot/knowledge-probing/research

papersTODAY 04:00 UTC

Language-Guided Multimodal Foundation Model Targets Brain Signal Analysis

Researchers posted a preprint describing a multimodal foundation model that uses language guidance to handle brain signal analysis without task-specific retraining. The approach is presented as supporting zero-shot and multi-task settings, addressing limited generalization in existing end-to-end and pre-trained models. The work appears on arXiv under cs.AI and cs.LG.

arxiv/foundation-models/multimodal/neuroscience

papersTODAY 04:00 UTC

SyRHM combines symbolic reasoning and associative retrieval for zero-shot harmful meme detection

Researchers propose SyRHM, a method for detecting harmful memes without task-specific training data. It targets implicit harm that comes from mismatches between image and text or from cultural stereotypes, which tripped up earlier multimodal detectors. The approach adds symbolic-language reasoning alongside associative retrieval to improve zero-shot performance.

multimodal/safety/content-moderation/zero-shot

papersTODAY 04:00 UTC

Proprioception-Anchored Cross-Modal Pretraining for Zero-Shot Sim-to-Real Assembly

The paper introduces a pretraining method that uses proprioceptive signals as an anchor to align different sensory modalities, aiming to help robots handle contact-rich assembly tasks. Such tasks demand submillimeter precision and accurate force interpretation during sustained contact, which makes sim-to-real transfer difficult. The approach targets zero-shot deployment of policies trained in simulation onto real hardware. This is a replacement listing on arXiv cs.AI.

robotics/sim-to-real/pretraining/manipulation

papersTODAY 04:00 UTC

InfoAtlas: Foundation Model for Zero-Shot Statistical Dependence Estimation

A new arXiv paper introduces InfoAtlas, a foundation model designed to estimate statistical dependence between high-dimensional random variables without per-task training. The authors target the high computational cost of existing neural mutual information estimators, which usually rely on iterative optimization. The method aims to deliver zero-shot dependence estimates, potentially removing the need for task-specific tuning.

research/mutual-information/foundation-models/arxiv

papersTODAY 04:00 UTC

Study compares Complement Naive Bayes with zero- and few-shot LLMs

A new arXiv paper benchmarks Complement Naive Bayes against large language models used in zero-shot and few-shot settings, spanning four model families and a much larger classical baseline dataset. The authors ask whether classical methods like Naive Bayes should be retired as LLMs become more common in research computing. The results offer an empirical comparison of accuracy and cost between the two approaches.

benchmarks/naive-bayes/llm/research

papersSEP 12 04:00 UTC

Aslema System Enters NADI 2026 Shared Task on Intent Recognition and Slot Filling

A paper describes Aslema, a system submitted to the NADI 2026 Shared Task 5, which covers intent recognition and slot filling. The authors benchmark four omni LLMs in a zero-shot setting against fine-tuned alternatives. Reported results indicate that fine-tuned models hold an advantage for these tasks.

arxiv/nlp/benchmark/fine-tuning

papersSEP 11 04:00 UTC

Zero-shot rope manipulation framework combines safe wiggle action with system identification

Researchers propose "Wiggle and Go!", a two-stage method that lets a robot manipulate rope without prior training. A short, low-risk wiggling motion gathers data about the rope's dynamics, which is then used to plan a reliable dynamic throw. The approach targets tasks where a single error causes unrecoverable failure.

robotics/zero-shot/system-identification/manipulation

papersSEP 10 04:00 UTC

Zero-Shot Temporal Localisation of Audio Deepfakes in Multi-Speaker Conversations

A new arXiv paper addresses voice-cloning fraud in which just a sentence or two of a genuine multi-speaker conversation is swapped for synthetic audio. Rather than giving one real-or-fake verdict for an entire recording, the proposed method pinpoints the exact time spans of fake speech without needing labelled examples of the targeted fakes.

deepfakes/audio/detection/voice-cloning

papersSEP 10 04:00 UTC

SALT Method Improves Token-Level Representations in Cross-Lingual Sentence Encoders

A new arXiv paper introduces SALT, a technique for strengthening how individual tokens are represented within multilingual sentence encoders. These encoders are optimized to align whole sentences across many languages, supporting applications like translation mining and zero-shot learning for low-resource languages. The paper targets the weaker token-level alignment that results from this sentence-focused training.

multilingual/embeddings/nlp