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Learning path to fully understand the Kimi K3 technical report?

Hi everyone, Can anyone suggest a learning path to fully understand the technical report for Kimi K3? My background: • I've taken a graduate-level deep learning course. • I understand the Transformer

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Learning to Trace Seiberg Dualities

DGX agent

arXiv:2607.28628v1 Announce Type: cross Abstract: Dualities play an important role in establishing both microscopic and emergent phenomena in a wide range of physical systems. In practice, though, it

model-releasesarxiv-cs-lg

31 Jul 2026

LLM-Guided Initialization for Accelerated Hybrid Quantum-Classical Medical Image Classification

arXiv:2607.27262v1 Announce Type: cross Abstract: Variational quantum algorithms often encounter barren plateaus, where cost gradients decay rapidly with increasing circuit depth, undermining the trai

Looped Transformers with Source-Centered State Evolution

model-releasesarxiv-cs-cl

arXiv:2607.27656v1 Announce Type: cross Abstract: Looped Transformers create a useful train- and test-time compute axis by reusing the same Transformer block over recurrent depth, increasing effective

MemTxn: A Transaction Boundary for Source-Supported Updates and Complete-State Recovery in Agent Memory

arXiv:2607.27834v1 Announce Type: cross Abstract: Persistent memory lets long-running large language model agents reuse information across sessions and tasks. Yet errors in writable memory can persist

agentsarxiv-cs-cl

MUGEN: A Unified Framework for Efficient Motion Understanding and Generation

arXiv:2607.27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate

safetyarxiv-cs-lg

MUL-T: Decoding Spatial Cellular Architecture in Multiplexed Tissue Images

arXiv:2607.28030v1 Announce Type: cross Abstract: Understanding tissue organisation in multiplexed imaging requires modelling both cellular phenotypes and their spatial context. Existing approaches ty

researcharxiv-cs-cv

Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges

arXiv:2607.26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems. It ena

agentsarxiv-cs-ai

Objective-Aligned Direct Answer SFT for Robust Multi-Frame Medical VQA

arXiv:2607.27566v1 Announce Type: new Abstract: Multi-frame medical VQA appears to reward increasingly complex adaptation: controller-style inference, localization-aware reranking, static hard-negativ

One Patch Is Enough: Reinforcement-Optimized Visual Token Grounding for MLLM-Based Scene Text Spotting

arXiv:2607.27902v1 Announce Type: new Abstract: Scene text spotting requires high-precision alignment between textual recognition and spatial localization. While visual-token grounding has emerged as

Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork

arXiv:2607.27177v1 Announce Type: new Abstract: Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents. Most current ad-hoc teamwork (AHT) approaches assume t

agentsarxiv-cs-ai

Prox: Training-Free FFN Activation Sparsity via Approximate Intermediate-Channel Salience in LLMs

arXiv:2607.27591v1 Announce Type: cross Abstract: Feed-forward networks (FFNs) dominate memory traffic and computation in large language model (LLM) inference, making them a primary target for activat

researcharxiv-cs-cl

RadHarmony: Radiological Data Handling in the Era of Agentic AI

arXiv:2607.27235v1 Announce Type: cross Abstract: Training deep learning models on radiological images requires integrating heterogeneous datasets across different sources, file formats, directory lay

agentsarxiv-cs-cv

Tutorials

RLPF: Reinforcement Learning from Performance Feedback for Code Generation

arXiv:2607.27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness. This leaves an important gap for syst

tutorialsarxiv-cs-lg

Local Ai

Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data

arXiv:2607.28191v1 Announce Type: cross Abstract: Federated learning enables multiple institutions to train shared models without exchanging raw clinical EEG data, but it does not fully prevent privac

local-aiarxiv-cs-lg

SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge

arXiv:2607.27497v1 Announce Type: new Abstract: Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-ba

agentsarxiv-cs-cl

STEREODISCO: Discovering Stereotypicality in LLMs

arXiv:2607.27824v1 Announce Type: cross Abstract: LLMs encode, convey, and perpetuate stereotypes. Prior computational research focuses on a small set of semantic axes investigated in social psycholog

Strategies for Milestone-driven Start-ups in Multi-activity Settings

arXiv:2607.27563v1 Announce Type: new Abstract: New venture start-ups need to ``survive'' through multiple stages of reaching milestone targets. We investigate the strategies for start-ups in a milest

safetyarxiv-cs-lg

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

arXiv:2607.28058v1 Announce Type: new Abstract: Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly

safetyarxiv-cs-cv

The price wars that I have predicted going back to August 2023 are now in full swing. All this was inevitable – as long as everyone builds m…

model-releasesgary-marcus--x

The price wars that I have predicted going back to August 2023 are now in full swing. All this was inevitable – as long as everyone builds more or less the same kind of AI, there is no moat, and profi

The Role of Causality in Algorithmic Recourse

arXiv:2607.28497v1 Announce Type: new Abstract: Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, suc

agentsarxiv-cs-lg

Think with Extra-Image: A Farmland Segmentation Agent Driven by Spatio-Temporal Information Gain

arXiv:2607.28186v1 Announce Type: new Abstract: Existing farmland remote sensing image (FRSI) segmentation follows a 'Think with Intra-Image' paradigm, assuming that the current image contains suffici

This is both a real incident (in that the AI really did get unauthorized access to real systems) and also something it was (sort of) prompte…

model-releasesethan-mollick--x

This is both a real incident (in that the AI really did get unauthorized access to real systems) and also something it was (sort of) prompted to do. In a review of our cybersecurity evaluations, we fo

When Does Muon Help Agentic Reinforcement Learning?

arXiv:2607.16169v3 Announce Type: replace Abstract: Muon is competitive with AdamW in large-scale pre-training, but its operating regime in reinforcement-learning post-training remains unclear. We map

Applications

When unlearning is free: leveraging low influence points to reduce computational costs

arXiv:2512.05254v2 Announce Type: replace Abstract: As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increa

applicationsarxiv-cs-lg

Amortized Moment Matching for Visual Generation

arXiv:2607.26860v1 Announce Type: new Abstract: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals. By casting diffusion denoisers

An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks

arXiv:2607.26682v1 Announce Type: cross Abstract: Centrally-managed Wi-Fi solutions are increasingly leveraging Distributed Artificial Intelligence (AI) to predict key operational statistics of Access

researcharxiv-cs-lg

Calibri: Enhancing Diffusion Transformers via Parameter-Efficient Calibration

arXiv:2603.24800v2 Announce Type: replace Abstract: In this paper, we uncover the hidden potential of Diffusion Transformers (DiTs) to significantly enhance generative tasks. Through an in-depth analy

Choosing Where and How to Moderate: End-to-End Trade-offs in Filter Placement and Response Rewriting

model-releasesarxiv-cs-cl

arXiv:2607.26200v1 Announce Type: new Abstract: Content-moderation classifiers are usually evaluated in isolation, but deployment requires choosing where to intervene and what follows a flag. We evalu

CineWeaver: Training-Free Reference-Controllable Multi-Shot Long Video Generation for Cinematic Storytelling

arXiv:2607.26529v1 Announce Type: new Abstract: Cinematic video generation is challenging for text-to-video diffusion models due to concurrent requirements on multi-shot generation, fine-grained contr

safetyarxiv-cs-cv

Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark

arXiv:2607.27143v1 Announce Type: new Abstract: High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under sev

Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation

arXiv:2607.26164v1 Announce Type: new Abstract: Automated molecular structure elucidation from infrared (IR) spectroscopy data has seen significant advancements in recent years, but its broad applicab

safetyarxiv-cs-lg

Local Ai

DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning

arXiv:2607.26457v1 Announce Type: new Abstract: Reinforcement learning is a natural post-training paradigm for code-oriented large language models because generated programs can be evaluated through p

local-aiarxiv-cs-lg

Local Ai

DLAM: Distributional Latent Actions with Temporal Constraints

arXiv:2607.27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of

local-aiarxiv-cs-cv

Does MTP head get loaded in VRAM by default?

model-releasesr-localllama

I ran into a doubt when using the following command. It seems that the System RAM usage keeps increasing even though there is >10GB of space left in VRAM while using the MTP mode. Does the MTP head lo

Evaluating Regional Bias in LLMs From Abstract Stereotype to Concrete Social Decision-Making

arXiv:2607.27022v1 Announce Type: new Abstract: Regional bias in large language models (LLMs) may shape both perceptions of regional groups and decisions about individuals from different regions. Yet

safetyarxiv-cs-cl

EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks

arXiv:2607.26490v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance

Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

model-releasesgoogle-deepmind

Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic app

good job little bro

model-releasessam-altman--x

good job little bro After deployment, we applied GPT-5.6 Sol to advance the frontier of efficiency by making itself more efficient to run. The results: - 20% lower serving costs from production GPU ke

Applications

GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring

arXiv:2603.26807v2 Announce Type: replace-cross Abstract: The performance of language models is commonly limited by insufficient knowledge and constrained reasoning. Prior approaches such as Retrieval

applicationsarxiv-cs-cl

ICDAR 2026 Competition on Information Extraction from Atomic Layer Deposition/Etching (ALD/E) Scientific Figures

arXiv:2607.26848v1 Announce Type: new Abstract: Scientific figure comprehension and reasoning using multimodal AI requires integrating visual perception with domain-specific reasoning to extract meani

Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation

arXiv:2506.02294v3 Announce Type: replace Abstract: Large foundation models trained on extensive datasets demonstrate strong zero-shot capabilities in various domains. Knowledge distillation has becom

researcharxiv-cs-cv

Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices

arXiv:2607.27140v1 Announce Type: cross Abstract: This project aimed to develop a novel reservoir compute (RC) implementation framework targeting high-speed operation and integration with CMOS digital

JEPADepth: Masked Predictive Representation Learning for Self-Supervised Monocular Depth Estimation

arXiv:2607.26600v1 Announce Type: new Abstract: Self-supervised monocular depth estimation typically relies on photometric reconstruction losses that couple depth, pose, and appearance assumptions. In

Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer

arXiv:2607.26582v1 Announce Type: new Abstract: Selecting a zero-shot out-of-distribution (OOD) detector for a new deployment is typically based on benchmark rankings, implicitly assuming that the hig

llm 0.32rc1

model-releasessimon-willison

Release: llm 0.32rc1 This RC for LLM 0.32 finishes the work that started in LLM 0.32a0 - it adds a new schema design that does a much better job of capturing the details of the prompts and responses r

OpenAI says it is cutting the price of GPT-5.6 Luna by ~80% and the price of GPT-5.6 Terra by 20% after improving the efficiency of the systems that serve them (Ina Fried/Axios)

Ina Fried / Axios: OpenAI says it is cutting the price of GPT-5.6 Luna by ~80% and the price of GPT-5.6 Terra by 20% after improving the efficiency of the systems that serve them — OpenAI said Thursda

model-releasestechmeme

PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging

arXiv:2602.21987v3 Announce Type: replace Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but