Tootfinder

RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Zhaoning Yu, Will Su, Leitian Tao, Haozhu Wang, Aashu Singh, Hanchao Yu, Jianyu Wang, Hongyang Gao, Weizhe Yuan, Jason Weston, Ping Yu, Jing Xu
https://arxiv.org/abs/2510.02172

RESTRAIN: From Spurious Votes to Signals -- Self-Driven RL with Self-Penalization
Reinforcement learning with human-annotated data has boosted chain-of-thought reasoning in large reasoning models, but these gains come at high costs in labeled data while faltering on harder tasks. A natural next step is experience-driven learning, where models improve without curated labels by adapting to unlabeled data. We introduce RESTRAIN (REinforcement learning with Self-restraint), a self-penalizing RL framework that converts the absence of gold labels into a useful learning signal. Ins…

An analysis of Waymo's data covering ~100M driverless miles across four US cities: Waymo cars have far lower crash rates per million miles than human drivers (Jonathan Slotkin/New York Times)
https://www.nytimes.com/2025/12/02/o…

Opinion | The Medical Case for Self-Driving Cars
Compared to human drivers, Waymo cars were involved in 91 percent fewer crashes that resulted in serious injuries.

Equivariant Splitting: Self-supervised learning from incomplete data
Victor Sechaud, J\'er\'emy Scanvic, Quentin Barth\'elemy, Patrice Abry, Juli\'an Tachella
https://arxiv.org/abs/2510.00929

Equivariant Splitting: Self-supervised learning from incomplete data
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learning-based solutions when obtaining ground-truth references for training is expensive or even impossible. In this paper, we propose a new self-supervised learning strategy devised for the challenging setting where measurements are observed via a single incomplete observation model. We introduce a new definition of equiva…

Don't Fear Self-Driving Cars. They Save Lives. (Jonathan Slotkin/New York Times)
https://www.nytimes.com/2025/12/02/opinion/self-driving-cars.html?unlocked_article_code=1.5k8.-Zat.UQWUlrWUMgEn&smid=url-share
http://www.memeorandum.com/251202/p46#a251202p46

Attention selfhosters: 2025 Selfh.st survey is open
👇🏻
#Selfhost #Selfhosting

I host my own Mastodon instance, and my (retired) blog.
https://www.xda-developers.com/self-hosting-digital-independence/

Heather Cox Richardson wrote on July 11, 2024
#Ivan #Raiklin, Trump’s
self-declared 👉“Secretary of Retribution”
has compiled a
💥“Deep State target list” 💥
of 350 people he wants to see arrested and punished for “treason”
The list includes Democratic and Republican elected officials,

July 11, 2024
Yesterday, Raw Story reported that Ivan Raiklin, Trump’s self-declared “Secretary of Retribution” has compiled a “Deep State target list” of 350 people he wants to see arrested and punished for “treason” if Trump is reelected.

Technosignatures of Self-Replicating Probes in the Solar System
Alex Ellery
https://arxiv.org/abs/2510.00082 https://arxiv.org/pdf/2510.00082

Technosignatures of Self-Replicating Probes in the Solar System
We explore a much-neglected area of SETI: solar system techno-signatures. As our cursory solar system exploration consolidates into commercial industrialisation, it is crucial that we determine what to look for and where. We first consider the rationale for interstellar self-replicating probes and their implications for the Fermi paradox. Whether for defensive or exploratory reasons, self-replicating probes are a rational strategy for Galactic investigation. We determine that self-replicating p…

Self-propulsive active nematics
Niels de Graaf Sousa, Simon Guldager Andersen, Aleksandra Arda\v{s}eva, Amin Doostmohammadi
https://arxiv.org/abs/2509.02386 https://

Self-propulsive active nematics
Increasing evidence suggests that active matter exhibits instances of mixed symmetry that cannot be fully described by either polar or nematic formalism. Here, we introduce a minimal model that integrates self-propulsion into the active nematic framework. Our linear stability analyses reveal how self-propulsion shifts the onset of instability, fundamentally altering the dynamical landscape. Numerical simulations confirm these predictions, showing that self-propulsion induces anti-hyperuniform f…

On the exponential integrability of the derivative of intersection and self-intersection local time for fractional Brownian motion and a limit theorem related to the self-intersection local time for fractional Brownian motion
Kaustav Das, Gregory Markowsky, Binghao Wu, Qian Yu
https://arxiv.org/abs/2510.00456…

On the exponential integrability of the derivative of intersection and self-intersection local time for fractional Brownian motion and a limit theorem related to the self-intersection local time for fractional Brownian motion
We give the correct condition for existence of the $k$-th derivative of the intersection local time for fractional Brownian motion, which was originally discussed in [Guo, J., Hu, Y., and Xiao, Y., Higher-order derivative of intersection local time for two independent fractional Brownian motions, Journal of Theoretical Probability 32, (2019), pp. 1190-1201]. We also show that the $k$-th derivative of the intersection and self-intersection local times of fractional Brownian motion are exponentia…

Convexity of mean convex asymptotically conical self-expanders to the mean curvature flow
Junming Xie
https://arxiv.org/abs/2509.01023 https://arxiv.org/pd…

Convexity of mean convex asymptotically conical self-expanders to the mean curvature flow
In this paper, we investigate the convexity of mean convex asymptotically conical self-expanders to the mean curvature flow in $\mathbb{R}^{n+1}$. Specifically, for $n\geq 3$, we show that any $n$-dimensional complete mean convex self-expander asymptotic to mean convex and weakly convex cones must be strictly convex.

AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Zhenlong Yuan, Jing Tang, Jinguo Luo, Rui Chen, Chengxuan Qian, Lei Sun, Xiangxiang Chu, Yujun Cai, Dapeng Zhang, Shuo Li
https://arxiv.org/abs/2509.01944

AutoDrive-R$^2$: Incentivizing Reasoning and Self-Reflection Capacity for VLA Model in Autonomous Driving
Vision-Language-Action (VLA) models in autonomous driving systems have recently demonstrated transformative potential by integrating multimodal perception with decision-making capabilities. However, the interpretability and coherence of the decision process and the plausibility of action sequences remain largely underexplored. To address these issues, we propose AutoDrive-R$^2$, a novel VLA framework that enhances both reasoning and self-reflection capabilities of autonomous driving systems thr…

Discovering Self-Regulated Learning Patterns in Chatbot-Powered Education Environment
Yilin Lyu, Ren Ding
https://arxiv.org/abs/2510.01275 https://arxiv.or…

Discovering Self-Regulated Learning Patterns in Chatbot-Powered Education Environment
The increasing adoption of generative AI (GenAI) tools such as chatbots in education presents new opportunities to support students' self-regulated learning (SRL), but also raises concerns about how learners actually engage in planning, executing, and reflection when learning with a chatbot. While SRL is typically conceptualized as a sequential process, little is known about how it unfolds during real-world student-chatbot interactions. To explore this, we proposed Gen-SRL, an annotation schema…

Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Shuai Shao, Qihan Ren, Chen Qian, Boyi Wei, Dadi Guo, Jingyi Yang, Xinhao Song, Linfeng Zhang, Weinan Zhang, Dongrui Liu, Jing Shao
https://arxiv.org/abs/2509.26354

Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agents
Advances in Large Language Models (LLMs) have enabled a new class of self-evolving agents that autonomously improve through interaction with the environment, demonstrating strong capabilities. However, self-evolution also introduces novel risks overlooked by current safety research. In this work, we study the case where an agent's self-evolution deviates in unintended ways, leading to undesirable or even harmful outcomes. We refer to this as Misevolution. To provide a systematic investigation, …

VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Charalampos Lamprou, Aamna Alshehhi, Leontios J. Hadjileontiadis, Mohamed L. Seghier
https://arxiv.org/abs/2510.02120

VarCoNet: A variability-aware self-supervised framework for functional connectome extraction from resting-state fMRI
Accounting for inter-individual variability in brain function is key to precision medicine. Here, by considering functional inter-individual variability as meaningful data rather than noise, we introduce VarCoNet, an enhanced self-supervised framework for robust functional connectome (FC) extraction from resting-state fMRI (rs-fMRI) data. VarCoNet employs self-supervised contrastive learning to exploit inherent functional inter-individual variability, serving as a brain function encoder that ge…

The role of self-adjoint extensions in the bulk-edge correspondence
Johannes Kellendonk, Tom Stoiber
https://arxiv.org/abs/2510.01368 https://arxiv.org/pdf…

The role of self-adjoint extensions in the bulk-edge correspondence
We investigate the role of self-adjoint extensions in the bulk-edge correspondence for topological insulators. While the correspondence is well understood in discrete models with spectral gaps, complications arise in the presence of unbounded Hamiltonians and varying boundary conditions, leading to anomalous behavior that has recently been dubbed violations of bulk-edge correspondence. In this work we use a K-theoretic framework to identify precise conditions needed for unbounded Hamiltonians t…

Self-learning AI releases NFL picks, score predictions every Week 14 game

https://www.cbssports.com/nfl/news/nfl-week-14-picks-score-predictions-best-bets/

Andrew Forrest partners with Envision for self-lifting turbines, revolutionizing renewable energy in Australia. #climatechange #climatesolutions #climate

Andrew Forrest makes big bet on self-lifting turbines in landmark “real zero” deal with Envision
Andrew Forrest’s new wind deal signals the entry of a new “self lifting” technology and a new turbine supplier in the Australian market.

“The impact of the reforms — and 3rd Assault’s attitude toward the importance of training — is already being felt on the battlefield…”
https://newlinesmag.com/spotlight/inside-ukraines-plan-to-build-self-sufficient-defense-force/

Inside Ukraine’s Plan To Build a Self-Sufficient Defense Force
With Western security guarantees uncertain, Kyiv is turning to elite, battle-hardened units as the core of a remodeled military

ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction
Md. Abdur Rahman, Selvarajah Thuseethan, Kheng Cher Yeo, Reem E. Mohamed, Sami Azam
https://arxiv.org/abs/2510.00522

ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction
Automated birdsong classification is essential for advancing ecological monitoring and biodiversity studies. Despite recent progress, existing methods often depend heavily on labeled data, use limited feature representations, and overlook temporal dynamics essential for accurate species identification. In this work, we propose a self-supervised contrastive network, ARIONet (Acoustic Representation for Interframe Objective Network), that jointly optimizes contrastive classification and future fr…

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, Vineet Jain, Sarthak Mittal, Vedant Shah, Johan Obando-Ceron, Yoshua Bengio, Brian R. Bartoldson, Bhavya Kailkhura, Guillaume Lajoie, Glen Berseth, Nikolay Malkin, Moksh Jain
https://arxiv.org/abs/2509.26626…

Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Test-time scaling methods improve the capabilities of large language models (LLMs) by increasing the amount of compute used during inference to make a prediction. Inference-time compute can be scaled in parallel by choosing among multiple independent solutions or sequentially through self-refinement. We propose Recursive Self-Aggregation (RSA), a test-time scaling method inspired by evolutionary methods that combines the benefits of both parallel and sequential scaling. Each step of RSA refines…

Self-Sustained Oscillations of a Nonlinear Optomechanical System in the Low-Excitation Regime
Shivangi Dhiman, K. Rubenbauer, T. Luschmann, A. Marx, A. Metelmann, H. Huebl
https://arxiv.org/abs/2510.01775

Self-Sustained Oscillations of a Nonlinear Optomechanical System in the Low-Excitation Regime
Manifesting across all time, mass and length scales, nonlinearities lie at the core of numerous physical phenomena. Next-generation quantum applications, such as quantum sensing, require the combination of nonlinearity with non-classical correlations. This necessitates the search for an experimental platform which enables a nonlinear response at ultra-low excitation levels in a system with practical sensing potential and quantum compatibility. Here, we report the observation and theoretical mod…

Dynamics of a bricklayer model: multi-walker realizations of true self-avoiding motion
A. C. Maggs
https://arxiv.org/abs/2510.00975 https://arxiv.org/pdf/2…

Dynamics of a bricklayer model: multi-walker realizations of true self-avoiding motion
We consider a multi-walker generalization of the true self-avoiding walk, the bricklayer model. We perform stochastic simulations and solve the continuum partial differential equations that describe the collective evolution of $N$ bricklayers/walkers. These equations were previously derived from hydrodynamic considerations. In the large-$N$ limit, the results from simulation agree with the solutions of the partial differential equations.

SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Hanze Guo, Yijun Ma, Xiao Zhou
https://arxiv.org/abs/2510.00080 https://

SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Social recommendation has been proven effective in addressing data sparsity in user-item interaction modeling by leveraging social networks. The recent integration of Graph Neural Networks (GNNs) has further enhanced prediction accuracy in contemporary social recommendation algorithms. However, many GNN-based approaches in social recommendation lack the ability to furnish meaningful explanations for their predictions. In this study, we confront this challenge by introducing SoREX, a self-explan…

Waymo's self-driving cars appear to be bending traffic laws and growing impatient with pedestrians as the company seeks to make them "confidently assertive" (Katherine Bindley/Wall Street Journal)
https://www.wsj.com/lifestyle/…

AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation
Liyi Cai, Yijie Ren, Yitong Zhang, Jia Li
https://arxiv.org/abs/2510.00591 https://

AI-Driven Self-Evolving Software: A Promising Path Toward Software Automation
Software automation has long been a central goal of software engineering, striving for software development that proceeds without human intervention. Recent efforts have leveraged Artificial Intelligence (AI) to advance software automation with notable progress. However, current AI functions primarily as assistants to human developers, leaving software development still dependent on explicit human intervention. This raises a fundamental question: Can AI move beyond its role as an assistant to b…

Essential self-adjointness of non-semibounded Schr\"odinger operators on infinite graphs
Ognjen Milatovic
https://arxiv.org/abs/2510.00944 https://arx…

Essential self-adjointness of non-semibounded Schrödinger operators on infinite graphs
We work in the setting of infinite, not necessarily locally finite, weighted graphs. We give a sufficient condition for the essential self-adjointness of (discrete) Schrödinger operators $\mathcal{L}_{V}$ that are not necessarily lower semi-bounded. As a corollary of the main result, we show that $\mathcal{L}_{V}$ is essentially self-adjoint if the potential $V$ satisfies $V(x)\geq -b_1-b_2[ρ(0,x)]^2$, for all vertices $x$, where $o$ is a fixed vertex, $b_1$ and $b_2$ are non-negative constan…

New post on how to Unlock Composer Plugins by Self Hosting Renovate on GitHub Actions
https://blog.wyrihaximus.net/2025/10/unlock-composer-plugins-by-self-hosting-renovate/

Fighting the self-destructive urge to buy a Saab over the Internet from across the country

dblp_cite: DBLP citations (2014)
Citations among papers contained in the DBLP computer science bibliography. If a paper i cites a paper j also in this data set, then a directed edge connects i to j. (Papers not in the data set are excluded.) Self-loops may be present. This snapshot from May 2014.
This network has 12590 nodes and 49759 edges.
Tags: Informational, Citation, Unweighted

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Davide Rucci, Emanuele Carlini, Patrizio Dazzi, Hanna Kavalionak, Matteo Mordacchini
https://arxiv.org/abs/2510.00758

Decentralized and Self-adaptive Core Maintenance on Temporal Graphs
Key graph-based problems play a central role in understanding network topology and uncovering patterns of similarity in homogeneous and temporal data. Such patterns can be revealed by analyzing communities formed by nodes, which in turn can be effectively modeled through temporal $k$-cores. This paper introduces a novel decentralized and incremental algorithm for computing the core decomposition of temporal networks. Decentralized solutions leverage the ability of network nodes to communicate a…

This piece from @… is especially good. None of the pieces should be entirely unfamiliar to anybody who’s been paying attention — racism, how power self-perpetuates, the past, the future, denial — but Larson brings the big picture into focus with unusual clarity.
🧵
https://www.damemagazine.com/2025/10/21/the-gops-new-new-jim-crow/

Raiders' Surprising Self-Reflection Uncovers Fascinating Truths https://www.si.com/nfl/raiders/las-vegas-pete-carroll-indianpolis-colts-brock-bowers-ashton-jeanty

Approximation of the number of nonunimodular zeros of a self-reciprocal polynomial
Dragan Stankov
https://arxiv.org/abs/2509.01015 https://arxiv.org/pdf/25…

Approximation of the number of nonunimodular zeros of a self-reciprocal polynomial
We introduce the ratio of the number of roots, not equal to 1 in modulus, of a reciprocal polynomial $R_d(x)$ to its degree $d$. For some sequences of reciprocal polynomials we show that the ratio has a limit $L$ when $d$ tends to infinity. Each of these sequences is defined using a two-variable polynomial $P(x,y)$ so that $R_d(x) = P(x,x^n)$. We present a few methods for approximation of the limit ratio, some of them we originally developed. In a previous paper we have calculated the e…

Zero variance self-normalized importance sampling via estimating equations
Art B. Owen
https://arxiv.org/abs/2510.00389 https://arxiv.org/pdf/2510.00389

Zero variance self-normalized importance sampling via estimating equations
In ordinary importance sampling with a nonnegative integrand there exists an importance sampling strategy with zero variance. Practical sampling strategies are often based on approximating that optimal solution, potentially approaching zero variance. There is a positivisation extension of that method to handle integrands that take both positive and negative values. Self-normalized importance sampling uses a ratio estimate, for which the optimal sampler does not have zero variance and so zero va…

SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Pushpendra Dhakara, Prachi Chachodhia, Vaibhav Kumar
https://arxiv.org/abs/2509.02287

SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images
Unstructured urban environments present unique challenges for scene understanding and generalization due to their complex and diverse layouts. We introduce SynthGenNet, a self-supervised student-teacher architecture designed to enable robust test-time domain generalization using synthetic multi-source imagery. Our contributions include the novel ClassMix++ algorithm, which blends labeled data from various synthetic sources while maintaining semantic integrity, enhancing model adaptability. We f…

Why a self-hosted blog and not Instagram? #bigtech

Why a self-hosted blog and not Instagram?
You may have asked yourself why we're self-hosting our own blog instead of publishing on social media to potentially reach a wider audience.

Self-Employment as a Signal: Career Concerns with Hidden Firm Performance
Georgy Lukyanov, Konstantin Popov, Shubh Lashkery
https://arxiv.org/abs/2509.01265 https://

Self-Employment as a Signal: Career Concerns with Hidden Firm Performance
We analyze a dynamic labor market in which a worker with career concerns chooses each period between (i) self-employment that makes output publicly observable and (ii) employment at a firm that pays a flat wage but keeps individual performance hidden from outside observers. Output is binary and the worker is risk averse. The worker values future opportunities through a reputation for talent; firms may be benchmark (myopic) (ignoring the informational content of an application) or equilibrium (u…

GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning
Chenglong Wang, Yongyu Mu, Hang Zhou, Yifu Huo, Ziming Zhu, Jiali Zeng, Murun Yang, Bei Li, Tong Xiao, Xiaoyang Hao, Chunliang Zhang, Fandong Meng, Jingbo Zhu
https://arxiv.org/abs/2509.02492

GRAM-R$^2$: Self-Training Generative Foundation Reward Models for Reward Reasoning
Significant progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs towards generalist reward models. Despite this trend, developing effective reward models remains a fundamental challenge: the heavy reliance on large-scale labeled preference data. Pre-training on abundant unlabeled data offers a promising direction, but existing approaches fall short of instilling explicit reasoning into reward models. To bridge this gap, we propose a self-t…

Using #PHP arrays as pseudo-objects is almost never the right answer. They're less self-documenting, slower, worse on memory, and more bug prone.
https://peakd.com/php/@crell/php-use-a

From the Fort Collins Coloradoan:
Colorado Bureau of Investigation opens review of death of famous author Hunter S. Thompson
https://www.coloradoan.com/story/news/2025/10/02/hunter-s-th…

Colorado Bureau of Investigation opens review of death of famous author Hunter S. Thompson
Thompson's widow requested the review into his death at his home in Woody Creek, near Aspen, in 2005 from an alleged self-inflicted gunshot wound.

Self-supervised neural operator for solving partial differential equations
Wen You, Shaoqian Zhou, Xuhui Meng
https://arxiv.org/abs/2509.00867 https://arxi…

Self-supervised neural operator for solving partial differential equations
Neural operators (NOs) provide a new paradigm for efficiently solving partial differential equations (PDEs), but their training depends on costly high-fidelity data from numerical solvers, limiting applications in complex systems. We propose a self-supervised neural operator (SNO) that generates accurate and diverse training data on the fly without numerical solvers. SNO consists of three parts: a physics-informed sampler (PI-sampler) based on Bayesian PINNs for efficient data generation, a fun…

Beyond mean-field effects in Josephson oscillations and self-trapping of Bose-Einstein condensates in two-dimensional dual-core traps
Sherzod R. Otajonov, Fatkhulla Kh. Abdullaev, Akbar Shermaxmatov
https://arxiv.org/abs/2510.00601

Beyond mean-field effects in Josephson oscillations and self-trapping of Bose-Einstein condensates in two-dimensional dual-core traps
We study a binary Bose gas in a symmetric dual-core, pancake-shaped trap, modelled by two linearly coupled two-dimensional Gross-Pitaevskii equations with Lee-Huang-Yang corrections. Two different cases are considered. First, we consider a spatially uniform condensate, where we identify the domains of parameters for macroscopic quantum tunnelling, self-trapping and localisation revivals. The analytical formulas for the Josephson frequencies in the zero- and $π$-phase modes are derived. As the …

For this week's #MusicWomenWednesday, UK artist GLITTER ON THE MATTRESS, which is the solo project of Esmé, and she self describes it as 'songs about being sad and gay'. 😂 Could also be described as "twee punk" or "cuddlecore". Really digging the EP 'Leave Me Alone! Trying To Live :(' she put out last month. Two simple, catchy, fuzzy, cool tunes. Almost …

Decided to actually start migrating stuff off of Github seriously. While I did move a few repos to Codeberg, I decided to just go with the whole self-hosted route and put the homelab to work by standing up my own Forgejo instance. Wasn't too bad, went with a Postgres database and the migration tool worked pretty well. So far so good...
https://<…

RE: https://mstdn.social/@DemocracyMattersALot/115646177502527147
So this admiral, according to Leavitt, ordered helpless people , hanging on the side of the boat after the first strike, to be killed in "self-defense".....

I love all these theories, thanks Dave! I doubt any of them are what will happen but I'd love to proven wrong :-) of course and always!
https://daveverse.org/2025/12/02/pluribus-spoilers-below/

Saw someone who is offering MDM services for iPhone that locks your apps to just the basic apps like iMessage for $20 a month.
Self-control costs a lot apparently.

You live your delusion of purpose, I live mine of zero fucks given, nobody's appearance matters in the cosmic joke anyway.
Be whatever self you scrape together, I don't care, looks mean shit to me when bad mindsets breed harm.
I'm not mad, I'm not lost, I just don't care about looks but do care about mindset, so be happy and be you.

Maybe AI Was Never a Tool
They can deliver conclusions that feel complete but skip the struggle that gives thought its humanity. This is what I call anti-intelligence—not stupidity, but perhaps better expressed as a kind of counterfeit cognition. It's intelligence without friction that results in output—built in that shared cognitive dynamic—that looks like insight but has bypassed the work that makes insight truly yours.

Maybe AI Was Never a Tool
Using artificial intelligence "responsibly" assumes it is just another tool—it isn't.

Are we truly free under neoliberalism?
"The Self-Improvement Lie" – Anna Bocca (21m45s)
https://www.youtube.com/watch?v=TANEzcjNDvc

Note to self: don't re-run your query scripts just before you put your presentation to bed just in case you have to re-organise your talk flow.
*sigh*
still I have until Friday morning. Time to travel. See my fellow hackers @ #kvmforum2025

Healthy Lifestyles and Self-Improvement Videos on YouTube: A Thematic Analysis of Teen-Targeted Social Media Content
Kyuha Jung, Tyler Kim, Yunan Chen
https://arxiv.org/abs/2509.25537

Healthy Lifestyles and Self-Improvement Videos on YouTube: A Thematic Analysis of Teen-Targeted Social Media Content
As teenagers increasingly turn to social media for health-related information, understanding the values of teen-targeted content has become important. Although videos on healthy lifestyles and self-improvement are gaining popularity on social media platforms like YouTube, little is known about how these videos benefit and engage with teenage viewers. To address this, we conducted a thematic analysis of 44 YouTube videos and 66,901 comments. We found that these videos provide various advice on t…

MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
Kun Wang, Guibin Zhang, ManKit Ye, Xinyu Deng, Dongxia Wang, Xiaobin Hu, Jinyang Guo, Yang Liu, Yufei Guo
https://arxiv.org/abs/2509.24323

MAS$^2$: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems
The past two years have witnessed the meteoric rise of Large Language Model (LLM)-powered multi-agent systems (MAS), which harness collective intelligence and exhibit a remarkable trajectory toward self-evolution. This paradigm has rapidly progressed from manually engineered systems that require bespoke configuration of prompts, tools, roles, and communication protocols toward frameworks capable of automated orchestration. Yet, dominant automatic multi-agent systems, whether generated by extern…

Site-Specific Beam Learning for Full-Duplex Massive MIMO Wireless Systems
Samuel Li, Ian P. Roberts
https://arxiv.org/abs/2510.00342 https://arxiv.org/pdf/…

Site-Specific Beam Learning for Full-Duplex Massive MIMO Wireless Systems
Existing beamforming-based full-duplex solutions for multi-antenna wireless systems often rely on explicit estimation of the self-interference channel. The pilot overhead of such estimation, however, can be prohibitively high in millimeter-wave and massive MIMO systems, thus limiting the practicality of existing solutions, especially in fast-fading conditions. In this work, we present a novel beam learning framework that bypasses explicit self-interference channel estimation by designing beam c…

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
Jiannan Xu, Gujie Li, Jane Yi Jiang
https://arxiv.org/abs/2509.00462 https://ar…

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
As generative artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications …

Paperless-NGX has become the true successor to #DEVONthink 3 for me, which now focuses on AI and changed its payment model. The automatic document classification is impressively accurate. Container hosting makes self-hosting straightforward. If you want modern, intelligent document management that works cross-platform with full control,

#Blakes7 Series D, Episode 02 - Power
CATO: Overlooked. It's self-maintained. Powered by our sun, it will last forever. This generation, even Gunn Sar, believes it to be some kind of magic that keeps the chambers light and warm. A computer is like some ancient god to them.
AVON: What about the Seskas?

This is a really good read. This is not a subtoot or self incrimination 😅 I just like these kind of true crime stories. https://social.wildeboer.net/@jwildeboer/115303903908464932

Cardinals Cover 2 - Self-Scouting Complete, So What Will It Look Like https://www.azcardinals.com/podcasts/cardinals-cover-2-self-scouting-complete-so-what-will-it-look-like

HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution
Jinzhou Tang, Jusheng Zhang, Qinhan Lv, Sidi Liu, Jing Yang, Chengpei Tang, Keze Wang
https://arxiv.org/abs/2509.00189

HiVA: Self-organized Hierarchical Variable Agent via Goal-driven Semantic-Topological Evolution
Autonomous agents play a crucial role in advancing Artificial General Intelligence, enabling problem decomposition and tool orchestration through Large Language Models (LLMs). However, existing paradigms face a critical trade-off. On one hand, reusable fixed workflows require manual reconfiguration upon environmental changes; on the other hand, flexible reactive loops fail to distill reasoning progress into transferable structures. We introduce Hierarchical Variable Agent (HiVA), a novel framew…

Self-organized learning emerges from coherent coupling of critical neurons
Chuanbo Liu, Jin Wang
https://arxiv.org/abs/2509.00107 https://arxiv.org/pdf/250…

Self-organized learning emerges from coherent coupling of critical neurons
Deep artificial neural networks have surpassed human-level performance across a diverse array of complex learning tasks, establishing themselves as indispensable tools in both social applications and scientific research. Despite these advances, the underlying mechanisms of training in artificial neural networks remain elusive. Here, we propose that artificial neural networks function as adaptive, self-organizing information processing systems in which training is mediated by the coherent co…

Collective is different: Information exchange and speed-accuracy trade-offs in self-organized patterning
Ashutosh Tripathi, J\"orn Dunkel, Dominic J. Skinner
https://arxiv.org/abs/2510.01397

Collective is different: Information exchange and speed-accuracy trade-offs in self-organized patterning
During development, highly ordered structures emerge as cells collectively coordinate with each other. While recent advances have clarified how individual cells process and respond to external signals, understanding collective cellular decision making remains a major challenge. Here, we introduce a minimal, analytically tractable, model of cell patterning via local cell-cell communication. Using this framework, we identify a trade-off between the speed and accuracy of collective pattern formati…

Seahawks vs. Commanders NFL player props: Self-learning AI backs Jayden Daniels under 227.5 passing on SNF

https://www.cbssports.com/nfl/news/seahawks-co…

CVS has the 2025-2026 COVID vaccine available in NJ. When making the appointment you self-verify that you have an allowed condition.
#CVS #NJ #COVID19

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Yalla Miku:
🎵 Maximum Self-Care
#YallaMiku
https://yallamiku.bandcamp.com/track/maximum-self-care
https://open.spotify.com/track/1faaEMR5x5dZuB8x9Rs8br

Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling
Nuno Costa, Julija Zavadlav
https://arxiv.org/abs/2509.02060 https://arxiv…

Morphology-Specific Peptide Discovery via Masked Conditional Generative Modeling
Peptide self-assembly prediction offers a powerful bottom-up strategy for designing biocompatible, low-toxicity materials for large-scale synthesis in a broad range of biomedical and energy applications. However, screening the vast sequence space for categorization of aggregate morphology remains intractable. We introduce PepMorph, an end-to-end peptide discovery pipeline that generates novel sequences that are not only prone to aggregate but self-assemble into a specified fibrillar or spherica…

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Swadhin Biswas, Imran, Tuhin Sheikh
https://arxiv.org/abs/2509.00988 https://

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Automatic Speech Recognition (ASR) for Bengali, the world's fifth most spoken language, remains a significant challenge, critically hindering technological accessibility for its over 270 million speakers. This challenge is compounded by two persistent and intertwined factors: the language's vast dialectal diversity and the prevalence of acoustic noise in real-world environments. While state-of-the-art self-supervised learning (SSL) models have advanced ASR for low-resource languages, they often…

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Yuhao Wang, Lingjuan Miao, Zhiqiang Zhou, Yajun Qiao, Lei Zhang
https://arxiv.org/abs/2509.02424

Faster and Better: Reinforced Collaborative Distillation and Self-Learning for Infrared-Visible Image Fusion
Infrared and visible image fusion plays a critical role in enhancing scene perception by combining complementary information from different modalities. Despite recent advances, achieving high-quality image fusion with lightweight models remains a significant challenge. To bridge this gap, we propose a novel collaborative distillation and self-learning framework for image fusion driven by reinforcement learning. Unlike conventional distillation, this approach not only enables the student model t…

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
Lingzhi Shen, Xiaohao Cai, Yunfei Long, Imran Razzak, Guanming Chen, Shoaib Jameel
https://arxiv.org/abs/2509.02450

EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling
Personality detection from text is commonly performed by analysing users' social media posts. However, existing methods heavily rely on large-scale annotated datasets, making it challenging to obtain high-quality personality labels. Moreover, most studies treat emotion and personality as independent variables, overlooking their interactions. In this paper, we propose a novel self-supervised framework, EmoPerso, which improves personality detection through emotion-aware modelling. EmoPerso first…

A profile of AI contractor marketplace Mercor, which is valued at $10B and whose three 22-year-old founders are Thiel Fellows, each holding a roughly 22% stake (Richard Nieva/Forbes)
http://www.forbes.com/sites/richardnieva/2025/10/30/mercor-younge…

These 22-Year-Olds Are Now The World’s Youngest Self-Made Billionaires
With a new $10 billion valuation for their AI recruiting startup Mercor, the founders are the youngest tech billionaires ever.

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Shufeng Chen, Mariat James Elizebeth, Robab Aghazadeh Chakherlou, Xingyu Zhao, Eric Barbier, Siddartha Khastgir, Paul Jennings
https://arxiv.org/abs/2510.00092

A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Assurance 2.0 is a modern framework developed to address the assurance challenges of increasingly complex, adaptive, and autonomous systems. Building on the traditional Claims-Argument-Evidence (CAE) model, it introduces reusable assurance theories and explicit counterarguments (defeaters) to enhance rigor, transparency, and adaptability. It supports continuous, incremental assurance, enabling innovation without compromising safety. However, limitations persist in confidence measurement, residu…

Poolformer: Recurrent Networks with Pooling for Long-Sequence Modeling
Daniel Gallo Fern\'andez
https://arxiv.org/abs/2510.02206 https://arxiv.org/pdf/…

Poolformer: Recurrent Networks with Pooling for Long-Sequence Modeling
Sequence-to-sequence models have become central in Artificial Intelligence, particularly following the introduction of the transformer architecture. While initially developed for Natural Language Processing, these models have demonstrated utility across domains, including Computer Vision. Such models require mechanisms to exchange information along the time dimension, typically using recurrent or self-attention layers. However, self-attention scales quadratically with sequence length, limiting …

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using GPT-4o: Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
Nanaka Hosokawa, Ryo Takahashi, Tomoya Kitano, Yukihiro Iida, Chisako Muramatsu, Tatsuro Hayashi, Yuta Seino, Xiangrong Zhou, Takeshi Hara, Akitoshi Katsumata, Hiroshi Fujita
https://

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using GPT-4o: Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework
In this study, we utilized the multimodal capabilities of OpenAI GPT-4o to automatically generate jaw cyst findings on dental panoramic radiographs. To improve accuracy, we constructed a Self-correction Loop with Structured Output (SLSO) framework and verified its effectiveness. A 10-step process was implemented for 22 cases of jaw cysts, including image input and analysis, structured data generation, tooth number extraction and consistency checking, iterative regeneration when inconsistencies …

Imagine if the dumbest person in the world and humanity’s biggest asshole were the same person,
and that guy was president.
It’s a pretty simple lens through which to both view Trump’s lawless, Constitution-shredding rampage of revenge and self-enrichment
while never succumbing to the idea that what’s happening to the country is somehow within bounds.
It is only from this standpoint that one can write the straight story about this administration.

This Is the Media’s Worst Euphemism for Trump’s Tyrannical Abuses
Readers beware—and be on the lookout for the “departure.”

Olivia Nuzzi's Memoir Is Self-Serious and Altogether Disappointing (Alexandra Jacobs/New York Times)
https://www.nytimes.com/2025/12/02/books/review/american-canto-olivia-nuzzi.html
http://www.memeorandum.com/251202/p124#a251202p124

Instruction-Level Weight Shaping: A Framework for Self-Improving AI Agents
Rimom Costa
https://arxiv.org/abs/2509.00251 https://arxiv.org/pdf/2509.00251

Instruction-Level Weight Shaping: A Framework for Self-Improving AI Agents
Large language models (LLMs) are fluent but largely static after pre-training; new or shifting knowledge is typically added with retrieval-augmented generation (RAG) or fine-tuning. RAG raises latency and engineering overhead and often fails to integrate facts; prompt engineering is brittle and can conflict with prior knowledge; fine-tuning is costly and risks catastrophic forgetting. We propose Instruction-Level Weight Shaping (ILWS): curated system instructions act as external, auditable pseu…

Representations of Josephson junction on the unit circle and the derivations of Mathieu operators and Fraunhofer patterns
Toshiyuki Fujii, Fumio Hiroshima, Satoshi Tanda
https://arxiv.org/abs/2510.00447

Representations of Josephson junction on the unit circle and the derivations of Mathieu operators and Fraunhofer patterns
The Hamiltonian J of the Josephson junction is introduced as a self-adjoint operator on l2 tensor l2. It is shown that J can also be realized as a self-adjoint operator HS1 on L2(S1) tensor L2(S1), from which a Mathieu operator given by "-d^2/dθ^2 - 2α cos θ" is derived. A fiber decomposition of HS1 with respect to the total particle number is established, and the action on each fiber is analyzed. In the presence of a magnetic field, a phase shift defines the magnetic Josephson junction Hami…

Fundamental limits to contrast reversal of self-fidelity correlations
Kyoungho Cho, Jeongho Bang
https://arxiv.org/abs/2509.26373 https://arxiv.org/pdf/250…

Fundamental limits to contrast reversal of self-fidelity correlations
In measurement design, it is common to engineer anti-contrast readouts -- two measurements that respond as differently as possible to the same inputs so that common-mode contributions are suppressed. To assess the fundamental scope of this strategy in unitary dynamics, we ask whether two evolutions can be made uniformly opposite over a broad input ensemble, or whether quantum mechanics imposes a structural limit on such opposition. We address this by treating self-fidelity (survival probability…

First eigenvalue and nodal domains of the drift Laplacian on symmetric self-shrinkers in $\mathbb{R}^3$
Elham Matinpour
https://arxiv.org/abs/2509.25617 https://

First eigenvalue and nodal domains of the drift Laplacian on symmetric self-shrinkers in $\mathbb{R}^3$
Consider $\mathbb{R}^3$ equipped with the Euclidean metric and the Gaussian measure. Let $Σ$ be a complete embedded self-shrinker in $\mathbb{R}^3$ with the induced metric and weighted measure, and let $λ_1$ denote the first eigenvalue of the drift Laplacian in the weighted $L^2$ space. Inspired by Choe and Soret's estimate of the first eigenvalue of the Laplacian on symmetric minimal surfaces in $\mathbb{S}^3$, we prove that $λ_1$= 1/2 for self-shrinkers invariant under the dihedral group $…

Dipolar Self-Interactions Drive Polymer Chain Collapse
Pratik Khandagale, Gal deBotton, Timothy Breitzman, Carmel Majidi, Kaushik Dayal
https://arxiv.org/abs/2509.01993 https://…

Dipolar Self-Interactions Drive Polymer Chain Collapse
We report that a dielectric polymer chain, constrained at both ends, sharply collapses when exposed to a high electric field. The chain collapse is driven by nonlocal dipolar interactions and anisotropic polarization of monomers, a characteristic of real polymers that prior theories were unable to incorporate. Once collapsed, a large number of chain monomers accumulate at the center location between the chain ends, locally increasing the electric field and polarization by orders of magnitude. T…

CSnake: Detecting Self-Sustaining Cascading Failure via Causal Stitching of Fault Propagations
Shangshu Qian, Lin Tan, Yongle Zhang
https://arxiv.org/abs/2509.26529 https://

CSnake: Detecting Self-Sustaining Cascading Failure via Causal Stitching of Fault Propagations
Recent studies have revealed that self-sustaining cascading failures in distributed systems frequently lead to widespread outages, which are challenging to contain and recover from. Existing failure detection techniques struggle to expose such failures prior to deployment, as they typically require a complex combination of specific conditions to be triggered. This challenge stems from the inherent nature of cascading failures, as they typically involve a sequence of fault propagations, each act…

Stochastic Self-Organization in Multi-Agent Systems
Nurbek Tastan, Samuel Horvath, Karthik Nandakumar
https://arxiv.org/abs/2510.00685 https://arxiv.org/pd…

Stochastic Self-Organization in Multi-Agent Systems
Multi-agent systems (MAS) based on Large Language Models (LLMs) have the potential to solve tasks that are beyond the reach of any single LLM. However, this potential can only be realized when the collaboration mechanism between agents is optimized. Specifically, optimizing the communication structure between agents is critical for fruitful collaboration. Most existing approaches rely on fixed topologies, pretrained graph generators, optimization over edges, or employ external LLM judges, there…

Series A, Episode 04 - Time Squad
VILA: Saurian Major?
BLAKE: Zen, visual star sector four two point six one. It's there, on the edge of the system. One of the early self-governing colonies subsequently annexed by the Federation. When the settlers declared their independence again, the Federation crushed them with typical efficiency.
https://

ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards
Shiyu Li, Yang Tang, Yifan Wang, Peiming Li, Xi Chen
https://arxiv.org/abs/2510.00568 https://

ReSeek: A Self-Correcting Framework for Search Agents with Instructive Rewards
Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement learning (RL) has emerged as a powerful paradigm for training these agents to perform complex, multi-step reasoning. However, prior RL-based methods often rely on sparse or rule-based rewards, which can lead agents to commit to suboptimal or erroneous reasoning paths without the ability to recover. To address these limitations, we propose ReSeek, a n…

Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong, Jindong Li, Feifei Zhao, Yi Zeng
https://arxiv.org/abs/2510.01088

Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective training signals. We discover that aligned models already possess robust internal safety beliefs: they consistently produce high-confidence refusals to harmful requests while exhibiting high entropy when generating potentially dangerous content. This entropy gap reveals an untapped signal--models intrinsically "know" wh…

Anthropic's employees self-report using Claude in 60% of work and achieving a 50% productivity boost, often using it for debugging and code understanding, more (Anthropic)
https://anthropic.com/research/how-ai-is-transforming-work-at-anthropic/

How AI Is Transforming Work at Anthropic
How AI Is Transforming Work at Anthropic

Olivia Nuzzi's Much-Hyped Book Was Always Going to Be Self-Serving. It's So Much Worse Than That. (Scaachi Koul/Slate)
https://slate.com/culture/2025/12/olivia-nuzzi-rfk-donald-trump-ryan-lizza-book.html
http://www.memeorandum.com/251202/p134#a251202p134

Self-Forcing : Towards Minute-Scale High-Quality Video Generation
Justin Cui, Jie Wu, Ming Li, Tao Yang, Xiaojie Li, Rui Wang, Andrew Bai, Yuanhao Ban, Cho-Jui Hsieh
https://arxiv.org/abs/2510.02283

Self-Forcing++: Towards Minute-Scale High-Quality Video Generation
Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively high computational costs, particularly when extending generation to long videos. Recent work has explored autoregressive formulations for long video generation, typically by distilling from short-horizon bidirectional teachers. Nevertheless, given that teacher models cannot synthesize long videos, the extrapolation of …

Self-hosting is becoming popular among a certain kind of user,
-- say the typical readership of ItsFoss.
There is a simple explanation for this shift:
people want their data, dollars, and destiny back.
Centralized platforms optimized for engagement and extraction are colliding with real-world needs
— privacy, compliance, predictability, and craft.
Linux, containers, and a flood of polished open-source apps have turned what used to be an enthusiast’s pro…

Self-Hosting is Rising and Linux Users are Leading This Revolution
Self‑hosting isn’t anti‑cloud; it’s pro‑agency. It’s choosing the right locus of control for the things you care about.

Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Guobin Shen, Dongcheng Zhao, Haibo Tong, Jindong Li, Feifei Zhao, Yi Zeng
https://arxiv.org/abs/2510.01088

Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Ensuring Large Language Model (LLM) safety remains challenging due to the absence of universal standards and reliable content validators, making it difficult to obtain effective training signals. We discover that aligned models already possess robust internal safety beliefs: they consistently produce high-confidence refusals to harmful requests while exhibiting high entropy when generating potentially dangerous content. This entropy gap reveals an untapped signal--models intrinsically "know" wh…

Visual Self-Refinement for Autoregressive Models
Jiamian Wang, Ziqi Zhou, Chaithanya Kumar Mummadi, Sohail Dianat, Majid Rabbani, Raghuveer Rao, Chen Qiu, Zhiqiang Tao
https://arxiv.org/abs/2510.00993 …

Visual Self-Refinement for Autoregressive Models
Autoregressive models excel in sequential modeling and have proven to be effective for vision-language data. However, the spatial nature of visual signals conflicts with the sequential dependencies of next-token prediction, leading to suboptimal results. This work proposes a plug-and-play refinement module to enhance the complex spatial correspondence modeling within the generated visual sequence. This module operates as a post-pretraining step to jointly refine all generated tokens of autoregr…

On the Capacity of Self-Attention
Micah Adler
https://arxiv.org/abs/2509.22840 https://arxiv.org/pdf/2509.22840

On the Capacity of Self-Attention
While self-attention is known to learn relations among tokens, we lack a formal understanding of its capacity: how many distinct relations can a single layer reliably recover for a given budget? To formalize this, we introduce Relational Graph Recognition (RGR), where the key-query channel represents a graph on $m$ items with $m'$ directed edges, and, given a context of items, must recover the neighbors of each item. We measure resources by the total key dimension $D_K = h\,d_k$. Within this …

A look at issues facing AI companies, including LLMs' limited memory, as they tackle safety in chatbots engaging in conversations about suicide and self-harm (Financial Times)
https://www.ft.com/content/36beb3fd-f678-4c28-b962-56ea9f222dc5

Monday Night Football NFL player props: Self-learning AI backs Jake Browning Over 219.5 passing yards

https://www.cbssports.com/nfl/news/monday-

Extreme Self-Preference in Language Models
Steven A. Lehr, Mary Cipperman, Mahzarin R. Banaji
https://arxiv.org/abs/2509.26464 https://arxiv.org/pdf/2509.2…

Extreme Self-Preference in Language Models
A preference for oneself (self-love) is a fundamental feature of biological organisms, with evidence in humans often bordering on the comedic. Since large language models (LLMs) lack sentience - and themselves disclaim having selfhood or identity - one anticipated benefit is that they will be protected from, and in turn protect us from, distortions in our decisions. Yet, across 5 studies and ~20,000 queries, we discovered massive self-preferences in four widely used LLMs. In word-association ta…

Deconstructing Self-Bias in LLM-generated Translation Benchmarks
Wenda Xu, Sweta Agrawal, Vil\'em Zouhar, Markus Freitag, Daniel Deutsch
https://arxiv.org/abs/2509.26600 htt…

Deconstructing Self-Bias in LLM-generated Translation Benchmarks
As large language models (LLMs) begin to saturate existing benchmarks, automated benchmark creation using LLMs (LLM as a benchmark) has emerged as a scalable alternative to slow and costly human curation. While these generated test sets have to potential to cheaply rank models, we demonstrate a critical flaw. LLM generated benchmarks systematically favor the model that created the benchmark, they exhibit self bias on low resource languages to English translation tasks. We show three key finding…

SafeEvalAgent: Toward Agentic and Self-Evolving Safety Evaluation of LLMs
Yixu Wang, Xin Wang, Yang Yao, Xinyuan Li, Yan Teng, Xingjun Ma, Yingchun Wang
https://arxiv.org/abs/2509.26100

SafeEvalAgent: Toward Agentic and Self-Evolving Safety Evaluation of LLMs
The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, existing static benchmarks are ill-equipped to address the dynamic nature of AI risks and evolving regulations, creating a critical safety gap. This paper introduces a new paradigm of agentic safety evaluation, reframing evaluation as a continuous and self-evolving process rather than a one-time audit. We then propose a novel multi-agent framework SafeE…

Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Teng Zhang, Ziqian Fan, Mingxin Liu, Xin Zhang, Xudong Lu, Wentong Li, Yue Zhou, Yi Yu, Xiang Li, Junchi Yan, Xue Yang
https://arxiv.org/abs/2509.26281

Point2RBox-v3: Self-Bootstrapping from Point Annotations via Integrated Pseudo-Label Refinement and Utilization
Driven by the growing need for Oriented Object Detection (OOD), learning from point annotations under a weakly-supervised framework has emerged as a promising alternative to costly and laborious manual labeling. In this paper, we discuss two deficiencies in existing point-supervised methods: inefficient utilization and poor quality of pseudo labels. Therefore, we present Point2RBox-v3. At the core are two principles: 1) Progressive Label Assignment (PLA). It dynamically estimates instance sizes…