Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning
Change captioning is the task of generating natural language descriptions that explain the changes between a pair of images.
Code that ran · page 3
Papers with a repository link where Syntology ran at least one harvested code sample. Each page shows two streams, newest first within each, counted separately: papers newer than the archive snapshot come from Syntology's graph Syntology; the rest are archive rows archive 2025-07-28. The two are never added together.
Newer than the archive snapshot Syntology
Cards 31–45 of 5,105 graph papers newer than 2025-07-28 with a Syntology-ran sample; this feed shows the newest 150, newest arXiv id first. Dates and the abstract sentence are from arXiv's metadata (CC0) for 5,105 of 5,105.
Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
Scientific knowledge about AI models is produced faster than the community can organize it.
StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?
Humans need to study only a handful of well-written textbooks to master a discipline and attempt its hardest problems.
MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks
Graph data across diverse domains can expose valuable relational information to unauthorized representation learning, creating a pressing need for protection against such misuse.
Beyond Language Priors: Diagnosing and Fixing Visual-Origin Hallucinations in Multimodal LLM
Existing research on object hallucination in multimodal large language models (MLLMs) predominantly attributes the problem to language priors such as over-reliance on textual co-occurrence statistics.
Faster Than Flash: Exploiting Attention Sparsity for Efficient Long-Context Decoding
The development of long-context Large Language Models (LLMs) is constrained by the memory bandwidth bottleneck and quadratic complexity of the attention mechanism during decoding.
Driving on Memory
End-to-end autonomous driving models plan future trajectories from raw sensor input.
UFPR-PEs: A Brazilian Face Recognition Benchmark with Self-Declared Race/Color Labels
While face recognition systems are widely deployed, ensuring their demographic reliability and robustness under uncontrolled visual conditions remains a critical challenge.
Learning Materials Properties from Scarce Labels and Unlabeled Crystals
Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery.
Cost-efficient Active Learning for Referring Image Segmentation and Grounding
Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target…
CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels
3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a desired frame remains largely manual.
SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature
Scientific reasoning remains challenging for open-source models, largely due to the lack of high-quality scientific reasoning data.
ATGS: Anchored Temporal Gaussian Splatting for Long Volumetric Video Representation
Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex motions, often leading to temporal instability and visual artifacts.
LoopArena: Benchmarking Models as Runtime Controllers for Loop Engineering
Loop Engineering is emerging as a practice for organizing development work around coding agents.
RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues--long, structured, and information-rich.
From the archive archive 2025-07-28 — filtered to papers Syntology ran Syntology
Cards 31–45 of 31,700 archive papers with a code link, filtered to those where Syntology ran at least one sample (the archive chip labels the rows, the Syntology chip labels the filter; the count is the archive rows that pass it); this feed shows the newest 150, archive date first (newest archive date 2025-07-17). Within a month, dated rows come first, then the 819 undated rows placed by the month in their arXiv id. 1 archive paper with neither a date nor an arXiv id cannot be placed and is not listed.
Flow-Anchored Consistency Models
Continuous-time Consistency Models (CMs) promise efficient few-step generation but face significant challenges with training instability.
RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic Agents
Large language models (LLMs) excel at logical and algorithmic reasoning, yet their emotional intelligence (EQ) still lags far behind their cognitive prowess.
Cautious Next Token Prediction
Next token prediction paradigm has been prevailing for autoregressive models in the era of LLMs.
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
Prompt injection attacks pose a significant security threat to LLM-integrated applications.
SIU3R: Simultaneous Scene Understanding and 3D Reconstruction Beyond Feature Alignment
Simultaneous understanding and 3D reconstruction plays an important role in developing end-to-end embodied intelligent systems.
Energy-Based Transformers are Scalable Learners and Thinkers
Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances.
Kwai Keye-VL Technical Report
While Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities on static images, they often fall short in comprehending dynamic, information-dense short-form videos, a dominant medium in today's…
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
Unified image restoration is a significantly challenging task in low-level vision.
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
Visual object tracking has gained promising progress in past decades.
LLaVA-SP: Enhancing Visual Representation with Visual Spatial Tokens for MLLMs
The architecture of multimodal large language models (MLLMs) commonly connects a vision encoder, often based on CLIP-ViT, to a large language model.
FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
Residual connection has been extensively studied and widely applied at the model architecture level.
SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning
Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approaches depend on human-curated…
Flash-VStream: Efficient Real-Time Understanding for Long Video Streams
Benefiting from the advances in large language models and cross-modal alignment, existing multimodal large language models have achieved prominent performance in image and short video understanding.
Dataset Distillation via Vision-Language Category Prototype
Dataset distillation (DD) condenses large datasets into compact yet informative substitutes, preserving performance comparable to the original dataset while reducing storage, transmission costs, and computational…
The feed is static: 10 pages of up to 15 cards per stream, rebuilt with the site. Older papers are reachable from task, dataset and method pages and from search. No repository stars are tracked and nothing here is ranked by popularity. Machine-readable twin: JSON.