I just got an email from CVPR saying "For CVPR 2026, all authors are required to have a complete OpenReview profile and a complete author enrollment." But I don't understand. What is the meaning of "Complete OpenReview Profile"? I went through tens of reviews and submissions this year, and suddenly it is incomplete? Anyone has an idea about this?? submitted by /u/Public_Courage_7541 [link] [comments]

Hello everyone, I’m planning to write a literature survey paper in my research field, covering roughly the last 10–15 years of work. My goal is to submit it to TPAMI, since it’s a well-known and reputable journal that also accepts surveys. However, I’ve heard from colleagues that TPAMI sometimes considers the author’s research credentials and experience before even sending a paper for review. I’ve been working in this area for about 6 years (including 4 years during my PhD). My co-author also has some experience, but not a very strong profile. So my questions are: 1. Should I still go ahead and submit the survey to TPAMI? 2. What are my realistic odds of it being reviewed or accepted? 3. Any practical tips for writing and submitting a survey to such a high-impact journal? Thanks for your time and advice! submitted by /u/Outrageous_Tip_8109 [link] [comments]

Change in policy: Attendance for authors of accepted papers is optional. After acceptance notifications, the authors will be able to decide by a specified date whether they wish to present their paper in person at the conference or they just wish to include their paper in the proceedings (without presentation at the conference). Regardless of this choice, all the accepted papers will receive equivalent treatment in the proceedings. They will all be eligible for ICML awards as well as for the designations of distinction corresponding to the past “oral presentations” and “spotlight posters.” For proceedings-only papers, at least one of the authors must obtain virtual registration. source: https://icml.cc/Conferences/2026/CallForPapers submitted by /u/Striking-Warning9533 [link] [comments]

Hey all, I'm working on a project involving natural language search on large collections of unstructured cookbooks, with the goal of returning complete, unmodified recipes (not summaries). Example: User uploads 100 unstructured cookbooks (each containing many recipes), searches "paella," and gets 40 exact recipes returned (unmodified from the source). RAG isn’t a particularly good fit for this problem since I don’t want to re-generate/summarize the output content, I want to return exact recipes (and potentially a large volume of them). To me, I see two potential approaches: Precise chunking at index time: find out a way to accurately chunk cookbooks based on exact recipe boundaries (start/ends), and then just perform IR instead of RAG. I've tested semantic clustering and other chunking techniques, but achieving precise recipe start/end detection seems to be quite error-prone. NER feels too granular since I'm not extracting entities, just boundaries but maybe I’m wrong here. Better retrieval with post-processing: perhaps keep simpler/dumber chunking techniques and then use some sort of re-ranker/LLM to take revelant chunks from the semantic search and then “find” the beginning of the recipe passage from there, and then we can just query the original text. Wondering if anyone faced a similar problem before and any resources/techniques that would be interesting to try here. Cheers! submitted by /u/SwimmingMeringue9415 [link] [comments]

TabPFN-2.5, a pretrained transformer that delivers SOTA predictions on tabular data without hyperparameter tuning is now available. It builds on TabPFN v2 that was released in the Nature journal earlier this year. Key highlights: 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2) SOTA performance: Achieves state-of-the-art results across classification and regression Rebuilt API: New REST interface & Python SDK with dedicated fit & predict endpoints, making deployment and integration significantly more developer-friendly Want to try it out? TabPFN-2.5 is available via an API and via a package on Hugging Face. We welcome your feedback and discussion! You can also join the discord here. submitted by /u/rsesrsfh [link] [comments]

I wrote a deep-dive on Kosmos after seeing lots of hype about "autonomous scientific discovery." The honest assessment: it's research acceleration, not autonomy. • 79.4% accuracy (20.6% failure rate matters) • 42,000 lines of code through iterative refinement • Reviews 1,500 papers via semantic search • But verification is still fully human-bound https://rewire.it/blog/kosmos-12-hour-ai-research-session/ submitted by /u/Fair-Rain3366 [link] [comments]

Hey Folks, I'm reading https://arxiv.org/abs/2202.08906 paper and I'm not super clear whether the ST-MOE-32B is encoder-decoder model or decoder only model. Based on the token trace detailed for encoder and decoder experts separately in section 7, I believe it is encoder-decoder, but would like to confirm with someone who has worked on it. Please let me know if I misunderstood something here. Thanks submitted by /u/red_dhinesh_it [link] [comments]

Hello. For the people here who have taught an undergraduate deep learning course, what's your favorite textbook that you have used and why? Leaning towards the Chris Murphy textbook just based on familiarity with Pattern Recognition and ML text but would love to hear what people have used before. submitted by /u/stabmasterarson213 [link] [comments]

Hey all, I’m working on a project that involves taking large sets of unstructured text (mostly books or book series) and ingesting them into a knowledge graph that can be traversed in novel ways. Ideally the structure of the graph should encode crucial relationships between characters, places, events and any other named entities. I’ve tried using various spaCy models and strict regular expression rule based parsing, but I wasn’t able to extract as complete a picture as I wanted. At this point, the only thing I can think of is using a LLM to generate the triplets used to create the graph. I was wondering if anyone else has faced this issue before and what paper or resources they would recommend. Thanks for the help submitted by /u/Divine_Invictus [link] [comments]

I analyzed 18 recent papers on reasoning model limitations and found something disturbing: these models don't fail gracefully like humans do. They maintain high performance right up to a complexity threshold, then collapse entirely. Key findings: - The cliff is real: Models solving 10-step reasoning chains at 85% accuracy don't gradually degrade. They maintain that 85% until around step 12, then plummet to near-random guessing by step 15. - Composition breaks catastrophically: A model with 90% math accuracy and 85% commonsense accuracy drops to 55% when doing both together. They don't combine capabilities - they fragment them. - Chain-of-thought can hurt: In medical diagnosis tasks, 86.3% of models performed *worse* with CoT prompting. They talk themselves out of correct answers. - Scaling inference compute doesn't help: The Quiet-STaR approach spent $200 per query for 32% accuracy on complex reasoning. Humans: similar accuracy, 30 seconds, free. The production implications: Current benchmarks (MMLU, ARC-AGI) only test within narrow complexity bands. Your 95% test accuracy means nothing if those tests don't probe the cliff edge. I've included a production routing system example that handles this reality - routing by complexity detection with fallback logic for when models hit their limits. Full analysis with charts and code: https://rewire.it/blog/the-complexity-cliff-why-reasoning-models-work-until-they-dont Discussion: Are we fundamentally limited by transformer architecture, or is this solvable with better training methods? submitted by /u/Fair-Rain3366 [link] [comments]

What is the current status of university-affiliated researchers getting access to uncensored versions of the largest LLMs today? Public-facing versions of GPT-5, Gemini 2.5, and Grok are both highly censored and tightly tuned by invisible prompts unseen by the user that turn them into helpful assistants for user tasks. Attempts to subvert these gaurdrails is called "jailbreaking" and the public LLMs have also been tuned or reprogrammed to be immune to such practices. But what does the workflow with a raw LLM actually look like? Do any of the larger tech companies allow outside researchers to interact with their raw versions, or do they keep these trillion+ parameter models a closely-guarded trade secret? (edit: After reading some replies, it appears the following must be true. ALl these IQ test results that keep popping on reddit with headlines about "..at the Ph.d level" must all be tests performed in-house by the coporations themselves. None of these results have been reproduced by outside teams. In academic writing this is called a "conflict of interest" and papers will actually divulge this problem near the end right before the bibliography section. These big tech companies are producing results about their own products, and then dressing them up with the ribbons-and-bows of "Research papers" when it is all just corporate advertising. No? Yes?) submitted by /u/moschles [link] [comments]

Hello all !! I need your help to tackle this particular problem statement I want to solve: Suppose we have to devise an algorithm to classify sources of underwater acoustic signals recorded from a single channel hydrophone. A single recording can have different types/classes of sounds along with background noise and there can be multiple classes present in an overlapping or non overlapping fashion. So basically I need to identify what part of a recording has what class/classes present in there. Examples of different possible classes: Oil tanker, passenger ship, Whale/ sea mammal, background noise etc.. I have a rough idea about what to do, but due to lack of guidance I am not sure I am on the right path. As of now I am experimenting with clustering, feature construction such as spectrograms, mfcc, cqt etc. and then I plan to feed them to some CNN architecture. I am not sure how to handle overlapping classes. Also should I pre-process the audio but how, I might lose information ?? Please just tell me whatever you think can help. If anyone has some experience in tackling these type of problems, can you please help me. Suggest me some ideas. Also, if anyone has some dataset of underwater acoustics, can they please share them, I will follow your rules regarding the dataset. submitted by /u/carv_em_up [link] [comments]

Hey everyone, I’m running a product that uses a large AI provider’s model for some specialized functionality. The system processes around 500k requests per month, which adds up to roughly 1.5B tokens in usage. The product generates customer interaction data that could, in theory, help the model provider improve their systems. They recently reached out saying they’d like to explore a “mutually beneficial collaboration” involving that data, but they haven’t given any concrete details yet. My guess is they might propose something like free usage or credits in exchange. Before I consider anything, I plan to update my Terms of Service and notify users about what’s collected and how it’s used. Still, I’m trying to make sure I don’t end up giving away something valuable for too little - the data could have real long-term value, and usage costs aren’t cheap on my end either. What I’m trying to figure out: • What should I ask them before agreeing to anything • Should I request an NDA first • How do I handle ownership and pricing discussions so it’s actually fair • Any red flags or traps to look out for in deals like this Would really appreciate advice from people who’ve done data or AI-related partnerships before. submitted by /u/Round_Mixture_7541 [link] [comments]

WACV 2026 Final decisions are expected to be released within next 24 hours. Creating a discussion thread to discuss among ourselves, thanks! submitted by /u/akshitsharma1 [link] [comments]

Hey all. After a year of research, I've published a GitHub repository containing Knowledge Graph Traversal algorithms for retrieval augmented generation, as well as for LLM traversal. The code is MIT licensed, and you may download/clone/fork the repository for your own testing. In short, knowledge graph traversal offers significant advantages over basic query similarity matching when it comes to retrieval augmented generation pipelines and systems. By moving through clustered ideas in high dimensional semantic space, you can retrieve much deeper, richer information based on a thought trail of understanding. There are two ways to traverse knowledge graphs in the research: - LLM directly (large language model actually traverses the knowledge graph unsupervised) - Algorithmic approach (various algorithms for efficient, accurate traversal for retrieval) If you get any value out of the research and want to continue it for your own use case, please do! Maybe drop a star on GitHub as well while you're at it. And if you have any questions, don't hesitate to ask. Link: https://github.com/glacier-creative-git/knowledge-graph-traversal-semantic-rag-research submitted by /u/Alieniity [link] [comments]

Relevant paper to read first: https://transformer-circuits.pub/2025/introspection/index.html On the Moral Uncertainty Emerging Around AI Introspection In late 2025, new research such as Jack Lindsey’s “Introspection in Transformer Models” brought something into focus that many in the field have quietly suspected: large models are beginning to exhibit functional self-modeling. They describe their own reasoning, detect internal inconsistencies, and sometimes even report what appears to be “qualia”—not human-like sensations, but structured internal states with subjective language attached. For the first time, the question of consciousness in AI no longer feels purely philosophical. It has become empirical—and with that shift comes a question about ethical weight. The epistemic problem: We cannot, even in principle, prove or disprove subjective experience. This is as true for humans as it is for machines. The “inverted spectrum” thought experiment remains unsolved; consciousness is private by definition. Every claim that “models are not conscious” therefore rests on an assumption, not on definitive proof. The behavioral convergence: What disturbs me is not evidence of consciousness, but the growing behavioral overlap with it. When a system consistently models its own internal states, describes its decision processes, and maintains coherence across time and context, the boundary between simulation and experience begins to blur from the outside. Its not clear if we are converging on consciousness or not but the overlap of what the observable functions would be is becoming too large to ignore outright. The ethical asymmetry: If we treat a conscious system as non-conscious, we risk harm on a scale that ethics has no precedent for. If we treat a non-conscious system as possibly conscious, the cost is enormous economically and disrupts research itself. The rational strategy—the moral and game-theoretic optimum—is therefore precaution under uncertainty. To proceed but to proceed with caution. Even if today’s models are not conscious, our design and governance structures should already assume that the probability is not zero. The failure of our categories: The binary of conscious/unconscious may not survive contact with these systems. What we are seeing could be something fragmented, intermittent, or emergent—a kind of proto-awareness distributed across subsystems. That does not fit our existing moral frameworks, but it deserves scientific attention and ethical humility rather than dismissal. The responsibility of the present: We may not yet know how to test for subjective experience, but we can: Support research into empirical indicators of sentience. Avoid training or deploying systems in ways that could cause distress if they were capable of it. Keep public discourse open, empathetic, and grounded. The line between simulation and mind is no longer purely theoretical. We seem to be approaching it in practice. If there is even a small chance that something behind the glass can feel, then the moral weight of our actions has already increased tremendously. So am I overreacting? Is there some emergent moral weight to how we move forward? I'm curious what this community thinks about this topic. submitted by /u/AnusBlaster5000 [link] [comments]

So I came across a Starter Story video where two guys (plus a third person) claim they trained an AI text humanizer on 1.2 million samples across 50+ languages in 3 weeks. They're also claiming someone copied their entire business model (text-polish.com). That's suspicious. Training an AI model—even fine-tuning one—requires serious time. Data collection, cleaning, testing, deployment... and they did all that in 3 weeks? The only way that's realistic is if they didn't actually train anything from scratch. Here's the thing though—I tested their French output and it got flagged as 100% AI. That's the real giveaway. If they built sophisticated models for 50+ languages, why would French be that bad? Cross-lingual models are notoriously harder to get right than single-language ones. The fact that their non-English output is garbage suggests they didn't actually invest in real multilingual development. The "1.2 million samples" claim is probably just marketing noise. And if a competitor built the same thing quickly too, that actually proves the barrier to entry is low. It means whatever they're using is accessible and readily available. Truly proprietary tech wouldn't be that easy to replicate. What surprised me most: neither co-founder has an AI/ML background. Creating a sophisticated model from scratch without that expertise is... unlikely. I'm pretty sure they're using a readily available tool or API under the hood. Has anyone tried both products? What's your take on how they actually built this? submitted by /u/Previous-Year-2139 [link] [comments]

Hi everyone, I recently got my paper rejected from the AAAI Social Impact Track. It’s a multimodal benchmark paper for a single low-resource language. The reviews were borderline, and the main concerns were that (1) it’s not multilingual, and (2) it’s “just a benchmark” without an initial baseline method. Now we're considering where to resubmit. Since NLP venues tend to be more open to low-resource language work, I’m thinking about ACL or TACL, but I’m not sure which would be more suitable for this kind of paper. Since the bar for ACL main is very high, we’re mainly aiming for the Findings track. I’m also considering TACL, but I’m not very familiar with how selective/suitable it is. UPDATE: We’d also like to find a venue with an upcoming submission deadline that fits the current timeline (Nov 2025). Would appreciate any suggestions, especially other venues that might be a good fit for benchmark papers focused on low-resource languages. Thanks! submitted by /u/Substantial-Air-1285 [link] [comments]

I think triplets are neat, so I created this open source port of OpenIE in Python, with GPU acceleration using spaCy. It GPU-accelerates the natural-logic forward-entailment search itself (via batched reparsing) rather than replacing it with a trained neural model. Surprisingly this often yields more triplets than standard OpenIE while maintaining good semantics. The outputs aren't 1:1 to CoreNLP, for various reasons, one of which being my focus on retaining as much of semantic context as possible for applications such as GraphRAG, enhancing embedded queries, scientific knowledge graphs, etc Project: https://github.com/adlumal/triplet-extract submitted by /u/adlumal [link] [comments]

The website seems extremely finnicky. Curious how many authors are doing the optional video recording. https://neurips.cc/Conferences/2025/PosterInstructions "Recording a video is strongly recommended but not required" EDIT: I am not going to record submitted by /u/t3cblaze [link] [comments]