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#zero-shot

8 curated 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

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

DiTAR+ Improves Decoding Stability in Autoregressive Diffusion Speech Synthesis

A new arXiv preprint introduces DiTAR+, a dual-optimization approach for continuous-latent autoregressive diffusion transformer models used in zero-shot speech generation. The method targets the limited decoding stability these models show when producing long utterances or handling complex linguistic input. No results beyond the abstract are described in the report.

speech-synthesis/diffusion-models/autoregressive/zero-shot
papersSEP 10 04:00 UTC

Zero-Shot World Models Shown to Learn About Physical Scenes as Efficiently as Children

A new arXiv paper in cs.AI argues that AI world models can capture core aspects of physical scenes, such as depth, motion, and how objects hold together and interact, without task-specific training. The authors draw an analogy to early childhood cognition, where humans build intuitive physics from relatively little experience. The work suggests developmental principles could guide the design of more sample-efficient artificial learners.

research/world-models/zero-shot/cognitive-science
papersSEP 10 04:00 UTC

Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

A new arXiv preprint proposes Semigroup-JEPA, a joint-embedding predictive architecture augmented with a latent dynamics consistency objective. The approach is designed to help world models capture physical behavior so that predicted dynamics remain plausible in unseen scenarios without retraining. The paper was posted to arXiv's cs.AI and cs.LG listings.

world-models/jepa/physics/zero-shot
papersSEP 12 04:00 UTC

Reification Approach Enables Zero-Shot Link Prediction With Standard GNNs

A new arXiv paper proposes moving the transfer mechanism used by knowledge graph foundation models out of specialized architectures and into the data representation itself. The authors treat reification as a transferable vocabulary, allowing plain graph neural networks to perform zero-shot link prediction on previously unseen knowledge graphs. This approach aims to match dedicated models such as ULTRA without requiring architecture-level hard-coding of transfer behavior.

knowledge-graphs/zero-shot/gnn/research
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