I built a small web app that lets you run tabular foundation models (currently just Google's TabFM) on spreadsheets without writing any code. Just drop in a CSV/Excel file, click a column header to mark what to predict, hit predict. Rows where the target cell is filled become the in-context examples and empty ones get predicted, right on the grid. A lot of people who'd benefit from these models aren't programmers, so I wrapped it in a UI anyone can use :) Repo: https://github.com/LckyLke/TabFMLabs Feedback very welcome! submitted by /u/Lckylke [link] [Kommentare]
Call for Papers and Demos Real-Time Conversational Agents (RTCA): Toward Natural Multimodal Interaction 1st RTCA Workshop [@]() NeurIPS 2026, Sydney, Australia 11 or 12 December 2026 Website: https://rtcaneurips26.github.io/ We are pleased to share the Call for Papers and Demos for the inaugural RTCA Workshop at NeurIPS 2026, focused on real-time multimodal conversational agents: streaming speech, video, and language generation; naturalness in interaction; and evaluation of live systems. Conversational AI has moved from text chat into the real world, voice modes that talk back, embodied avatars, agents that share our screens and tools. To feel natural, these systems must operate in real time, streaming while continuously listening, watching, and re-planning. This is fundamentally harder than offline generation: latency, turn-taking, backchannels, interruptions, and cross-modal alignment become first-class problems that the offline paradigm sidesteps. Recent progress on full-duplex speech–language models, real-time talking-head generation, and streaming ASR shows the regime is feasible, but the field still lacks shared benchmarks, vocabulary, and methodology for interactional naturalness. RTCA brings together researchers across speech, vision, language, HCI, social-signal processing, and ML systems around three intertwined questions: real-time generation under hard latency budgets, naturalness in interaction, and evaluation of live systems. Topics of Interest We invite original contributions on topics including (but not limited to): Streaming/low-latency speech synthesis, ASR, and full-duplex audio–language models Real-time talking-head, avatar, and embodied video generation; lip-sync, gaze, expressivity under streaming Streaming language models; incremental and speculative decoding for dialogue Turn-taking, backchanneling, interruption handling, and floor management Multimodal alignment under latency and partial-observation constraints Prosody, emotion, and paralinguistic generation in interactive settings Memory, grounding, and tool use during live conversation Evaluation of naturalness: perceptual studies, turn-taking metrics, perceived latency, interactive Turing-style tests Datasets and benchmarks for interactive (not offline) evaluation Efficient inference, on-device deployment, and the systems–quality trade-off Safety, identity, and trust in real-time agents (deepfakes, persuasion, consent) Submission Types We welcome: Full papers (up to 8 pages) — may be presented as posters and/or contributed talks. Short papers (up to 4 pages) — work in progress or focused contributions. Demo papers (Extended Abstracts or up to 2 pages) All submissions must use the NeurIPS 2026 style file and be formatted for double-blind review. Page limits exclude references and appendices. Papers must be submitted in PDF format via OpenReview (portal link to be published on the workshop website). The workshop is non-archival; authors retain the right to publish elsewhere. Important Dates (End of day, Anywhere on Earth) Call for papers opens: 18 July 2026 Submission deadline (papers and demos): 29 August 2026 Author notification: 29 September 2026 Workshop date: 11 or 12 December 2026 Organisers Niki Foteinopoulou — Tavus, United Kingdom Alessandro Conti — Tavus, Italy Jack Saunders — Tavus, United Kingdom Oya Celiktutan — King's College London, United Kingdom Cigdem Beyan — University of Verona, Italy Ioannis Patras — Queen Mary University of London, United Kingdom For more information, visit our website https://rtcaneurips26.github.io/ or contact us at [rtca-workshop@googlegroups.com](mailto:rtca-workshop@googlegroups.com). We look forward to your contributions! submitted by /u/Few-Ferret9700 [link] [Kommentare]
I have a technical interview for a machine learning position coming up, and I'm really nervous. Can you tell me about your experiences with this process? It's my first technical interview, and I don't know what to expect submitted by /u/Abatistta [link] [Kommentare]
Hello community, just a short reminder that Raffi Krikorian (CTO @ Mozilla) is live today for an AMA to discuss Mozilla's inaugural State of Open Source AI report. Topics include enterprise adoption, the real cost of "free"models, developer trust, Chinese open models and their impact, agentic AI infrastructure, and the future of open source with respect to Machine Learning & Artificial Intelligence. Drop your questions in the thread here: https://reddit.com/r/MachineLearning/comments/1upxdvc/raffi_krikorian_cto_mozilla_ama_on_the_state_of/ The AMA starts at 1pm ET/10am PT/6PM BST His team reached out to us and he provided proof via Linkedin here: https://www.linkedin.com/feed/update/urn:li:activity:7481380478365880321/ Thank you! submitted by /u/Benlus [link] [Kommentare]
For open-weight LLMs, how practical is it to study defenses against post-release fine-tuning that weakens refusal or safety behavior? I've been seeing “uncensored” or “heretic” variants of new models appear very quickly after release, which raises a question I’m curious about: is fine-tuning resistance a meaningful safety goal for open-weight releases, or is it too narrow because determined users can always modify weights, switch models, or use other workarounds? And to a larger extent, is current safety training even worth the cost and effort if it takes 30 minutes and an automated script to break the model? I’m not asking about a specific method, just the threat model. What would count as a useful practical win here? For example, would increasing attacker cost or making safety removal less reliable be valuable, even if perfect prevention is impossible? Curious how people think about this from a model release, governance, and AI safety perspective. submitted by /u/Aaron_Rock [link] [Kommentare]
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Attending ICML for the first time (virtually) next week as a 3rd year PhD student in the US. I want to get into industry after finishing and have heard a lot about the benefits of networking at conferences to build industry connections. How do you actually go about doing this? Are there gonna be industry reps at the conference who you just go up to and talk to, send LinkedIn connections, and get to know them? Previously only been to one domain-specific conference and had more students/academic people than industry. TLDR: What's the best way to network at these big AI/ML conferences, especially since I'm gonna be attending virtually, with the goal of working in industry as a RE/RS in a couple of years? Would really appreciate any insights or helpful advice. Thanks! submitted by /u/IronBlowers [link] [Kommentare]
Hello everyone! I'm posting our research work as you might be interested in how we used ML to map part of the brain cells of the human hippocampus :) We used various human brain slices at high resolution (1 micrometer per pixel) and developed a custom segmentation pipeline that uses SoTA whole slice cell segmentation networks, like CellPoseSAM with good zero shot performances. We then refined semi-automatically those annotations and ensembled more finetuned models within the pipeline, adding a merging algorithm and a cell classification for 3 classes (excitatory and inhibitory neurons, and glial cells). But the high-res slices covered only a few parts of the hippocampus with respect to other slices scanned at 20x less the resolution where the cell nuclei are only 1 pixel wide. So we tried to map the high-res annotations we obtained to the low-res corresponding slices, and used a small UNet to supervise a density estimation task for 3 classes. We obtained a network that outputs a density map that can be sampled to obtain a probabilistic map of the cellular positions. Finally, to reconstruct the volume, we stacked together all the low-resolution density maps from all the slices that covered the hippocampus and obtained a point cloud, which you can see in the GIF along the corresponding anatomical CA (Cornus Ammonis) areas. The performances are still limited by the quantity of data and low-resolution slices, but we showed that the results were biologically plausible given previous estimates by other researchers. The paper was accepted at MICCAI 2026 a few weeks ago! Feedback is very welcome, especially on the density-estimation formulation and possible uses of the generated point cloud. submitted by /u/V_ector [link] [Kommentare]
The Setlur et al result that scaling test time compute without verification or RL is provably suboptimal keeps showing up in my reading and I think it deserves more weight than the "yet another scaling paper" treatment it got. The core claim is that verifier based methods, RL or search guided by a verifier, dominate verifier free methods like distilling successful traces, given a fixed compute budget, and the gap widens as the test time budget grows. What I find underappreciated is how cleanly this maps onto what the deployed systems are now converging on. The single agent ReAct loop is the verifier free extreme, you sample a trace and keep it, maybe with some self reflection that is still the same model grading itself. The multi agent setups that actually move numbers split the verifier off into a separate process. Apodex is the most explicit example I have seen, they train the team behavior in and run a verification team, conflict reviewer, fact checker, draft reviewer, that does not share the reasoning trace, and the reported lift is coming from the verifier not from added parameters. Same trained model, heavy duty mode adds double digits on BrowseComp and FrontierScience-Research. That is exactly the regime the theory predicts, the verifier is where the gain lives. The reason I think this matters beyond benchmark watching is that it reframes where the next chunk of capability comes from. If you believe the VB over VF result, then the path is not just bigger models or longer traces, it is better verifiers that are structurally independent of the generator. The pseudo correctness framing fits here too. The failure mode the verifier has to catch is not the obvious hallucination, it is the answer that passes every self check but is still wrong, and that failure mode is invisible to any verifier that shares context with the generator. What I want to hear from others is the open questions. My list. How much of the verifier gain is transferable to domains without clean reward signals, since the math proof case is the easy one. Whether the independence has to be architectural, separate agents, or whether a sufficiently disciplined prompt separation on one model gets you most of the way. And whether the VB advantage keeps widening or saturates once the verifier itself becomes the bottleneck. The practical version of this for anyone building. If your agent loop has the same model reviewing its own work, you are in the VF regime and the theory says you are leaving capability on the table. The cheapest structural change is to make the verifier a different process with denied context, even if it is the same weights. submitted by /u/Mysterious_Sign_9501 [link] [Kommentare]
I'm looking for human-in-the-loop platforms that allow you to automatically transcribe audio followed by manually fixing the transcriptions and fine tuning the model. Is there a local (not an online service) installable platform for doing this? submitted by /u/neuralbeans [link] [Kommentare]
Hi, I'm thinking of building a small community of 10-15 people where we can help each other to learn something new. The primary focus will be on ML research and open-source projects. If you're interested, DM me. knowledge of machine learning is a plus, as want to keep this a high-impact, collaborative group. Only for the moderators, since my last post was removed and I was asked to post in the monthly hiring thread: This post is not related to hiring. If I post it in the monthly hiring thread, hardly anyone will see it, so it defeats the purpose. My last post was removed very quickly, but in the mean time I've received 3 comments and 3 DMs. This clearly shows that people are interested, so I kindly request that you don't remove this post. submitted by /u/Tall-Gold-3553 [link] [Kommentare]
I apologize for such an amateur question if someone is offended I just finished my 2nd year of degree. Well, the degree was a bit slow and I did the ML course this semester as well but being a Third World Country and stuff, it doesn't really matter cause I didn't learn antg of value from them I've been studying ML myself for 5-6 months, but I skipped the last 2 months cause of some issues and I've failed to get that motion back so I need a little bit of advices as where to continue I know python of course and I've learned many ML algorithms, all supervised and what you'd call easy. I have understood their general concepts and maths but never went in deep. I did them in practical as well. Made a very few projects. Now, I'm confused what should I learn next, I feel unsupervised learning isn't really my thing or I wouldn't be able to do it so can I just skip that? And idk what's next, so what is it? I've thought of learning Agentic AI as well but I can't do that until I'm satisfied with myself that I completely know ML and I can work on professional level. And if you've any resources to learn from, certifications etc as well. I'd really appreciate it. Again I apologize for really rookie questions. submitted by /u/Negative-Guard-4487 [link] [Kommentare]
I wanted to share an ICML 2026 spotlight position paper on a failure mode in image-generation alignment: aesthetic preference optimization may override explicit user intent when the requested output is anti-aesthetic or outside mainstream visual taste. The paper frames this as **reversed alignment**. Instead of the model aligning to the user's stated preference, the output is pulled back toward the model's learned aesthetic prior. We test generation and reward models on prompts asking for blurry, distorted, low-fidelity, negative-emotion, and other anti-aesthetic images. GitHub repo: https://github.com/weathon/icml2026_position Paper: https://arxiv.org/abs/2512.11883 OpenReview: https://openreview.net/forum?id=1gQ4zc1Q8I I would be interested in feedback on the framing and on evaluation designs for separating prompt understanding from preference override. submitted by /u/Striking-Warning9533 [link] [Kommentare]
Abstract: A sufficient account of how the neocortex learns must meet three criteria: Computationally, it must approximate a powerful, general-purpose learning algorithm known to scale to human-level intelligence; Algorithmically, it must be implementable using known, well-established neural circuits within the neocortex and associated brain structures; Implementationally, there must be a detailed account for how all of the algorithmic mechanisms actually function at a neurochemical level. At present, there is only one framework that meets all of these criteria: error-driven predictive learning via temporal derivatives, driven by corticothalamic circuits, based on competitive kinase synaptic plasticity induction mechanisms. This has been implemented in the Axon neural simulation framework using spiking neurons, and demonstrated to learn across a wide range of challenging cognitively motivated tasks. arxiv.org/abs/2606.08720 Something like this will lead to something better than back propagation and improve training times substantially. submitted by /u/Terminator857 [link] [Kommentare]
I am currently a cs graduate student at a top university after completing a comp eng undergrad there My thesis is more to do with embedded system security and maybe applied ml Unfortunately I didn’t get into any ml focused graduate program as they’re extremely competitive even with good grades from a top undergrad (and some projects of course) I want to do a PhD in ml as I’m currently having a tonne of fun taking optimization and ml courses - further I’ve been studying it for years already I think my interests lie mostly in optimization ie shampoo, soap, hessian free/approximations Also inference optimization is pretty cool but I’m more of a math person even though my undergrad was in computer engineering I enjoy learning about things like pca, lda, other stats techniques on my own I’ve had about 7 internships but they were all in software except one where I did a bit of ml near the end ie fitting decision trees to data Currently I couldn’t get a job after 1 year of applying so I’m in a masters program at my school. I have 0 publications and my supervisor puts out maybe 1 paper every 3 years so it’s unlikely I’ll get one from my thesis, if I’m lucky I could do some sort of anomaly detection paper but even that’s unlikely (t-test is pretty much unbeatable) What steps should I take to get into a PhD program after graduating and what classes should I take as my math background feels like it’s lacking when I read something like the shampoo paper submitted by /u/proturtle46 [link] [Kommentare]