Knowledge catalogue
arXiv:2605.00326v1 Announce Type: new Abstract: Single-prompt first-token probabilities from zero-shot vision-language model (VLM) safety classifiers are treated as decision scores, but we show they a
arXiv:2605.00099v1 Announce Type: cross Abstract: Despite rapid recent advances in quantum machine learning, the field is in many ways stuck. Existing approaches can exhibit serious limitations, and w
arXiv:2605.00623v1 Announce Type: new Abstract: This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar ene
arXiv:2407.16216v3 Announce Type: replace Abstract: Large language models (LLMs) trained via pretraining and supervised fine-tuning (SFT) can still produce harmful and misaligned outputs, or struggle
arXiv:2604.07669v2 Announce Type: replace Abstract: Lead optimization in drug discovery requires improving therapeutic properties while ensuring that molecular modifications correspond to feasible syn
arXiv:2605.00654v1 Announce Type: new Abstract: For a risk-averse finite-horizon Markov Decision Problem, we introduce a special class of Markov coherent risk measures, called mini-batch measures. We
arXiv:2605.00468v1 Announce Type: new Abstract: Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores
arXiv:2605.00380v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances reasoning of Large Language Models (LLMs) but usually exhibits limited generation diver
arXiv:2512.12744v3 Announce Type: replace Abstract: Activation sparsity offers a compelling route to accelerate large language model (LLM) inference by selectively suppressing hidden activations, yet
arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermedi
arXiv:2605.00787v1 Announce Type: new Abstract: While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy up
arXiv:2605.00444v1 Announce Type: new Abstract: Multi-modal large language models (MLLMs) advance vision language understanding but face inherent limitations in long-video tasks due to bounded percept
arXiv:2605.00401v1 Announce Type: new Abstract: Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused vi
arXiv:2605.00449v1 Announce Type: cross Abstract: Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explici
arXiv:2605.00069v1 Announce Type: new Abstract: Elastic distances like dynamic time warping (DTW) are central to time series machine learning because they compare sequences under local temporal misali
arXiv:2605.00553v1 Announce Type: new Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety. Finding effec
arXiv:2503.10990v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying th
arXiv:2511.07756v4 Announce Type: replace Abstract: Diffusion models initialize generation from an isotropic Gaussian latent, yet changing only the random seed can substantially alter prompt faithfuln
arXiv:2605.00182v1 Announce Type: new Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from
arXiv:2605.00365v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often
arXiv:2605.00658v1 Announce Type: new Abstract: Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often tr
arXiv:2605.00364v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existin
arXiv:2510.00072v2 Announce Type: replace Abstract: Training robust reasoning vision-language models (VLMs) in rare domains (such as geospatial) is fundamentally constrained by supervision scarcity. W
arXiv:2605.00719v1 Announce Type: new Abstract: Unsupervised deraining has attracted attention for its ability to learn the real-world distribution of rain without paired supervision. However, the lac
arXiv:2506.11991v3 Announce Type: replace-cross Abstract: In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which
arXiv:2509.21723v4 Announce Type: replace Abstract: Achieving generalizable bimanual manipulation requires systems that can learn efficiently from minimal human input while adapting to real-world unce
VR founder Jaron Lanier says a future where people are paid for the data they give AI is better than one where everyone depends on billionaires to survive 'artificial intelligence is a marketing term,
arXiv:2605.00155v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has become a core post-training step for aligning large language models, yet the reward signal used
Way stronger. Today was huge for Musk, bad for OpenAI. The case against OpenAI is getting markedly stronger now that Musk is off the stand. Why? Musk’s lawyer is interrogating OpenAI founder Greg Broc
Well-known, well-respected AI expert Stuart Russell is now testifying at the Musk-OpenAI trial – despite OpenAI’s BS attempts to exclude him. Read the backstory here: A new filing just dropped in the
arXiv:2605.00018v1 Announce Type: new Abstract: Data-driven MoCap-to-radar models generate plausible micro-Doppler spectrograms, but do they actually learn the underlying physics? We introduce a physi
Why do these influencers always say “just published” for papers published last year? This paper is great, and I have written extensively about it in my newsletter. But c’mon. Apple didn’t “just publis
arXiv:2605.00080v1 Announce Type: cross Abstract: World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They s
Gary Marcus reported that Greg Brockman, OpenAI's President, had a $10 million side deal with Sam Altman during OpenAI's early nonprofit period that was not disclosed to Elon Musk or documented in non
Ben Cohen / Wall Street Journal: A profile of BlackBerry's QNX division, whose operating system controls safety features in 275M cars and accounts for half of BlackBerry's revenue — John Wall has spen
agreed The core argument of my latest article 'There is no AI race' was that the 'AI race' between the US and China is a manufactured narrative concocted by US tech companies to serve their corporate
AI will create more jobs than any other technology in history. The doomers' fundamental error isn't just the lump of labor fallacy. It's deeper than that. They assume a finite problem space. This is t
Altman’s bait-and-switch: So, my problem with this is, Altman basically admits he's been running a confidence game for years about this singularity stuff, then pivots when it becomes inconvenient, and
Brian Moore's interactive clock comparison tool demonstrates the significant performance gaps and inconsistencies across different AI systems when performing the seemingly simple task of displaying ti
“In summary, there is very little evidence for LLMs benefiting patients or doctors for health outcomes” - Dr. @EricTopol Read his full review here: https://open.substack.com/pub/erictopol/p/the-parado
It’s been almost six weeks since Jensen Huang claimed we had achieved AGI. Why don’t I feel any different? https://www.forbes.com/sites/antoniopequenoiv/2026/03/23/nvidias-jensen-huang-says-he-thinks-
Joseph Gordon-Levitt argues that most AI systems are fundamentally built on 'mass theft,' contending that companies deploying large language models without proper authorization or compensation for tra
Old days: “Bigshot chief scientist of major corporation can’t handle criticism of the work he hypes” New days: “Same bigshot no longer hypes LLMs; can’t handle the fact that someone else noticed flaws
Thank god, indeed, that @ylecun publicly stood up and admitted he was wrong, to fight for the greater good. Oh wait. @GaryMarcus thank god he publicly stood up, admitted he was wrong and you were righ
that’s right. 😂 Finally, the great LeCun conversion arc is complete! “LLM agents = disaster” – said the guy who once defended Galactica like it was the Second Coming. Gary, you didn’t change… the time
Why is the AI backlash growing? Outside of coding (where there is clear value), and a handful of other domains (e.g. brainstorming), Generative AI has been a net negative for society. GenAI has been u
5 months later, I would say that AI is definitely on track to produce 90% of all bullshit within a few years. Congratulations LLM Industry, you are changing the world! Claim AI will generate 90% of kn
Are AIs about to subjugate humanity? In the debate about catastrophic AI risk, evolutionary scenarios receive far too little attention compared to largely speculative arguments about “instrumental con
This post likely refers to an academic honor or recognition that Richard Dawkins received but Daniel Dennett did not, posted as a commentary by cognitive scientist Gary Marcus on X (formerly Twitter).
Gary Marcus comments on financial incentives in AI discourse, suggesting that posting sensationalized or false claims about artificial intelligence could generate significant revenue. This reflects Ma
If only Richard Dawkins had seen this. 1/2 Why AI is unlikely to become conscious – my 2026 @TEDTalks is now online. What do you think about the prospects for 'conscious AI'? https://www.ted.com/talks
If you read Dawkins on AI and consciouness I hope you will take the time to also watch @anilkseth’s lucid TED talk on the same topic (link below). @RichardDawkins Hi @RichardDawkins - the illusion of
Indeed, it does seem strange. Far more skepticism seems to have been applied to the first idea than the second. @garydanteleigh @GaryMarcus I just can't wrap my head around the fact that this guy is s
Gary Marcus, a prominent AI researcher and skeptic, posted commentary on claims about conscious AI systems. The post likely discusses concerns about overstated assertions regarding machine consciousne
People are really enjoying our full workshops showing end to end walkthroughs of real production workflows! This is a rare double header with @braintrust's Giran Moodley and @OussamaHaff walking thoug
suno can spit bars 😵💫 https://open.spotify.com/track/2ZqWAGhGt13WAt4krXv8bV?si=c953d55900a8455f ♫ Bias Alignment I’m deriving the variance, bias alignment is delicate Scaling parameters, balancing gr
Gary Marcus discusses concerns about the massive financial investment being directed toward generative AI development and deployment, likely examining whether the trillion-dollar spending is justified
Gary Marcus expressed frustration on X about unexpectedly spending his Saturday morning writing a Substack article critiquing Richard Dawkins' recent work or statements. The post suggests Marcus felt
arXiv:2604.27162v1 Announce Type: cross Abstract: Reinforcement Learning (RL) algorithms exhibit high sample complexity, particularly when applied to Decentralized Partially Observable Markov Decision
arXiv:2503.07878v5 Announce Type: replace-cross Abstract: When we train models on biased datasets, they not only reproduce data biases, but can worsen them at test time - a phenomenon called bias ampl