KDD 2026 Cycle 2 Results [D]
KDD 2026 Cycle 2 Results covers the announcement of review decisions and outcomes from the second submission cycle of KDD 2026, which accepts papers across multiple tracks including Research, Applied
Knowledge catalogue
KDD 2026 Cycle 2 Results covers the announcement of review decisions and outcomes from the second submission cycle of KDD 2026, which accepts papers across multiple tracks including Research, Applied
Continual Harness proposes an approach to online adaptation for foundation agents that moves beyond traditional gradient-based retraining by introducing a dual-agent architecture (Teacher/Student) wit
This paper presents a method for controllable generation in flow matching models by adapting them through reference examples. The key insight is that the velocity field in flow matching is determined
This post discusses implementing Physics-Informed Neural Networks (PINNs) to solve the 3D heat equation within a C++ WebAssembly engine for real-time aerospace applications. The work combines machine
TabPFN-3 is a pre-trained tabular foundation model that supports datasets up to 1,000,000 rows × 200 features , representing a significant scaling improvement for the TabPFN family. The model delivers
Jensen-Shannon divergence is a method of measuring the similarity between two probability distributions. It is based on the Kullback-Leibler divergence, with the notable difference that it is symmetri
DeepSeek released the full technical report for DeepSeek-V4 on April 24, 2026, titled 'DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence.' The paper details FP4 quantization-awa
Mechanistic interpretability research aims to uncover specific neurons and circuits in neural networks responsible for tasks, but over a decade of efforts suggests these findings may not translate int
Recent advances in diffusion-based language models enable controllable sequence generation, but applying them to structured code remains challenging, prompting exploration of syntax-aware diffusion fr
This discussion examines the practical performance gains of quantization techniques when deployed in production environments, particularly addressing whether theoretical speedups translate to real-wor
ROCm has reached production-ready status for PyTorch and vLLM workloads in 2026. PyTorch runs well and MI300X benchmarks are competitive, though the ecosystem gap with CUDA remains real. AMD's MI355X
A study examining 71 AI scenarios found that AI experts (N=119) and the public (N=1100) hold differing views on AI risks, benefits, and value, with experts discounting risk influences more heavily tha
The NeurIPS Creative AI track became part of the main conference proceedings for 2025, with papers presented as posters during the conference , marking a change from 2024 when the track was not part o
This post likely describes a machine learning project implementing QLoRA (Quantized Low-Rank Adaptation) fine-tuning on the Qwen2.5-1.5B model to classify English language proficiency levels according
A discussion post on r/MachineLearning where users share experiences and advice about submitting machine learning research articles to ACM journals, with focus on publications like ACM Transactions on
This work presents an explainability library for transformer models that provides tools for understanding transformer behavior through attributions and concept-based explanations . The resource likely
This Reddit discussion covers technical challenges encountered when attempting to integrate Chebyshev filters—mathematical filters with equiripple characteristics used in signal processing—into convol
torch-nvenc-compress is a Python library that leverages GPU NVENC (NVIDIA's hardware video encoding) to optimize PCIe bandwidth utilization by compressing data during transfer. The project implements
This is a discussion thread from r/MachineLearning where community members ask and answer beginner-level and straightforward questions about machine learning concepts, techniques, and practical implem
Based on the Reddit discussion title, this likely discusses how to design and establish benchmarks for evaluating the quality of AI-generated responses in Sales Development Representative (SDR) system
Variational Joint Embedding (VJE) is a framework that synthesizes joint embedding and variational inference to enable self-supervised learning of probabilistic representations in a reconstruction-free
The 2nd Multilingual Conversational Speech Language Model (MLC-SLM) Challenge is an open research competition inviting teams worldwide to participate , featuring free registration with a $20K prize po
Production low-latency autocomplete implementations employ diverse strategies including inference server optimization (tools like vLLM, llama.cpp, NVIDIA Triton), deployment choices (cloud APIs, on-pr
The ACL Rolling Review (ARR) operates on a two-monthly review cycle , and the March 2026 cycle refers to one of these periodic submission and peer review rounds for computational linguistics research.
This post describes a project where the author leveraged Andrej Karpathy's compact GPT implementation to create a tool for converting pandas DataFrames into searchable context windows, which they then
This Reddit discussion post addresses a beginner's question about choosing between 3B and 7B parameter models for fine-tuning on multi-task reasoning problems. The post likely contains advice from exp
GPU Compass is an open-source tool that tracks and displays real-time GPU pricing information across over 20 cloud providers. The platform likely helps machine learning practitioners and researchers c
INT3 compression with fused Metal kernels enables large language models to compute attention operations directly on compressed (INT3/INT4) key-value cache representations using custom GPU kernels that
Production machine learning systems can experience performance degradation where predictions appear reasonable and metrics don't change immediately, yet decision quality decays over time in ways diffi
easyaligner is a forced alignment library designed to be performant and easy to use , leveraging GPU acceleration to align audio with text transcriptions. The tool supports flexible text normalization
Mooncake is the serving platform for Kimi developed by Moonshot AI, featuring a KVCache-centric disaggregated architecture that separates prefill and decoding clusters while leveraging underutilized C
This r/MachineLearning discussion post serves as a community-curated beginner's resource for applying artificial intelligence and machine learning to materials science, likely compiling recommended to
A researcher on r/MachineLearning built a political benchmark to evaluate how various LLMs handle sensitive geopolitical and politically contentious questions. Key findings include that Kimi K2 (Moons
This Reddit post discusses a known instability in PPO when advantage estimates operating across different temporal scales (e.g., short-horizon and long-horizon returns) are dynamically routed or mixed
This r/MachineLearning discussion examines a growing concern in the AI and gaming community: that certain video game 'simulators' may be deliberately designed — or repurposed — to harvest player actio
A Reddit post on r/MachineLearning sharing an open-source project and accompanying book by Sebastian Raschka that walks through implementing GPT-2, Llama 3, and DeepSeek from scratch using PyTorch, wi
This Reddit post on r/MachineLearning, tagged [R] (Research), is a community discussion thread sharing or reviewing peer feedback related to CHI PLAY — the international and interdisciplinary ACM SIGC
This r/MachineLearning discussion thread addresses the widespread challenge of reproducing results claimed in modern ML research papers, a topic of significant concern in the field. Community members
A community thread on r/MachineLearning where users are invited to share research ideas, project concepts, or suggestions related to machine learning. The '[N]' tag indicates it is a discussion post r
Moss is a YC-backed high-performance runtime for real-time semantic search that delivers sub-10ms lookups, instant index updates, and zero infrastructure overhead, running where the agent lives — clou
This Reddit discussion thread from r/MachineLearning explores the growing challenges faced by applicants from non-elite undergraduate institutions when applying to PhD programs in machine learning and
This r/MachineLearning discussion post examines LLM jailbreaks through the lens of social engineering, arguing that the psychological vulnerabilities found in LLMs are not random artifacts but structu
This Reddit post is a reminder to the r/MachineLearning community about an upcoming Ask Me Anything (AMA) session with Max Welling, Chief AI Officer at CuspAI and Professor of Machine Learning at the
This r/MachineLearning post discusses how self-hosted LLMs that comply with prompt injection attempts — effectively following malicious or overriding instructions embedded in user input — represent a
A community researcher shared on r/MachineLearning how they extended Chatterbox TTS — Resemble AI's open-source, 500M-parameter model — to support 8 Indian languages using LoRA (Low-Rank Adaptation),
This is a community discussion post on the r/MachineLearning subreddit where a researcher shares their proposed methodology for tackling Open Set Recognition (OSR) and/or novelty detection, inviting p
This Reddit thread from r/MachineLearning reflects community skepticism about the peer review standards at top ML conferences, with a user expressing surprise that a particular paper received an oral
This r/MachineLearning discussion thread explores the standards and expectations reviewers and program committees use when evaluating ML papers for acceptance at top venues such as NeurIPS, ICML, and
A r/MachineLearning discussion thread exploring the potential NLP applications of a large-scale dataset comprising over 20 million Indian legal documents, enriched with citation graphs and pre-compute
ClawBench is a benchmark of 153 everyday web tasks spanning 144 live platforms across 15 categories — from completing purchases and booking appointments to submitting job applications. Unlike existing
This r/MachineLearning post discusses research on training neural networks to recognize and express uncertainty by introducing a novel HALO-Loss function that gives models an explicit 'abstention' opt
No agent maintained moral reasoning consistency across scenarios. Findings from a structured study with 11 agents on classic ethical dilemmas [R]
This r/MachineLearning discussion post details a hands-on benchmark study in which TranslateGemma — Google's open translation model suite built on Gemma 3, available in 4B, 12B, and 27B sizes and cove
This r/MachineLearning Reddit thread serves as a community Q&A ('qq thread') focused on the Area Chair (AC) process for ICML, where researchers ask and answer questions about AC guidance, responsibili
This Reddit post from r/MachineLearning discusses the concept of decomposing machine learning models into a graph database representation, treating a model's components — such as layers, weights, and
A developer shared a project on r/MachineLearning showcasing an AI-powered tool designed to automate common data preprocessing tasks, including dataset cleaning, intelligent imputation of missing valu
This r/MachineLearning post discusses a Claude Code skill tailored for neurotechnology and brain-computer interface (BCI) machine learning workflows, likely covering domain-specific tasks such as neur
This Reddit thread on r/MachineLearning discusses the workshop paper accept/reject notifications for ECCV 2026, the European Conference on Computer Vision (ECCV), a biennial premier research conferenc
This r/MachineLearning discussion thread centers on a hands-on workshop covering context engineering — the process of designing and optimizing the information an AI agent sends to and receives from a
A researcher on r/MachineLearning documented an independent attempt to scale a pure Spiking Neural Network (SNN) to 1.088 billion parameters, built from scratch, exploring whether SNNs can achieve per