c/artificial-intelligence

Prompt injection has emerged as one of the most persistent failure modes in tool-using LLM systems, particularly in agentic workflows where models interact with external data sources. Most mitigation strategies focus on input filtering or model-side alignment, but these approaches struggle because the core issue is structural: Approach I explored a system-level mitigation strategy by introducing a middleware layer (Sentinel Gateway) that enforces a strict separation between: Instruction channel: trusted, runtime-issued commands Data channel: untrusted external inputs (web, files, APIs) Instead of attempting to classify malicious inputs, the system ensures that: All agent actions require a signed, scoped runtime authorization token, effectively decoupling observation from execution. Implementation FastAPI middleware layer for agent tool calls Token-based authorization for execution requests Streamlit interface for inspection and debugging Audit logging of agent decisions and tool usage Supports multi-agent integration patterns (e.g., Claude-based sessions) Local or Postgres-backed persistence layer Repo https://github.com/cmtopbas/Sentinel-Gateway Discussion question I’m interested in feedback on: whether instruction/data separation is a meaningful abstraction for agent safety failure modes in token-based execution gating how this compares conceptually to other agent safety or sandboxing approaches submitted by /u/vagobond45 [link] [Kommentare]

Today is the day you (🫵!) get access to 80TB plus of data from over 30 astronomical surveys in one place. 4GB of RAM is enough even at Gaia Scale. Check out our writeup here: https://huggingface.co/blog/hugging-science/multimodal-universe-hats And a tutorial here https://asciinema.org/a/1259218 submitted by /u/Smith4242 [link] [Kommentare]

For Job Postings please use this template Hiring: [Location], Salary:[], [Remote | Relocation], [Full Time | Contract | Part Time] and [Brief overview, what you're looking for] For Those looking for jobs please use this template Want to be Hired: [Location], Salary Expectation:[], [Remote | Relocation], [Full Time | Contract | Part Time] Resume: [Link to resume] and [Brief overview, what you're looking for] ​ Please remember that this community is geared towards those with experience. submitted by /u/AutoModerator [link] [Kommentare]

I recently came across the announcement for the MARS2 Workshop (Multimodal Reasoning Competition) at ECCV 2026. From what I understand, it focuses on multimodal reasoning and test-time reasoning (“slow thinking”), especially applied to video and real-world scenarios like advertising understanding and marketing-related tasks. The topic sounds interesting, but I’m still trying to wrap my head around what the actual evaluation setup looks like in practice. The speaker list includes researchers from MIT, Cambridge, Oxford, CMU, NTU, etc., which look solid. I also noticed Tec-Do and Minimax are listed as organizers/sponsors. I know a bit about MiniMax, but Tec-Do's research in CV and multimodal is new to me—anyone here familiar with them? ​Also, quick question for anyone working on video temporal grounding: do you think this kind of benchmark is actually helpful for practical dev, or is it mostly just academic/exploratory right now? Trying to decide if it's worth keeping on my radar. submitted by /u/Glass-Childhood-4971 [link] [Kommentare]

Hi everyone, I'm a final-year Computer Engineering student building a Flask-based AI Diabetic Retinopathy Detection system. The web application itself is complete with patient management, authentication, dashboard, PDF report generation, prediction history, and AI inference. The only issue I'm facing is with the AI model. I'm using a 5-class Diabetic Retinopathy classifier trained on the APTOS 2019 dataset. Classes: No DR Mild Moderate Severe Proliferative DR The model predicts all five classes, but the predictions are inconsistent. Examples: Moderate is sometimes classified as Severe or Proliferative. Severe is often classified as Moderate or Proliferative and is rarely predicted correctly. Some fundus images from outside the APTOS dataset produce completely unexpected results. The model sometimes shows very high confidence (90%+) even when the prediction appears incorrect. Things I've already tried: Different pretrained models (including a ResNet50 trained on APTOS) ResNet152 implementation Correct preprocessing (RGB conversion, resizing, normalization) Verified class mapping Softmax confidence scores Test-Time Augmentation (TTA) Image quality validation Top-3 predictions instead of only one prediction I'm trying to understand whether this is: A domain shift problem between APTOS and other datasets? A limitation of the pretrained model? A preprocessing issue? Class imbalance? Or simply expected behavior in 5-class DR classification? I'm also considering using an ensemble (ResNet50 + EfficientNet + DenseNet), but it's difficult to find compatible pretrained 5-class diabetic retinopathy models. I'd really appreciate advice from anyone who has worked on retinal image classification or medical AI. My questions are: Is this level of class confusion common in diabetic retinopathy models? What preprocessing techniques made the biggest improvement for you (CLAHE, retinal cropping, illumination correction, etc.)? Has anyone significantly improved results using ensemble models? Are there any high-quality pretrained 5-class DR models that you'd recommend? If you were in my situation, what would be the first thing you'd investigate to improve prediction consistency? Any suggestions, GitHub repositories, pretrained models, research papers, or personal experiences would be greatly appreciated. Thanks in advance! submitted by /u/Delicious_Corner_754 [link] [Kommentare]

Was reading some research papers put out by Anthropic (and some other organizations/researchers) and one thing I've noticed is that these research papers consistently all share the same quality: Oftentimes over 100 pages of pure words, interspersed with screenshots of very dense/hard to read prompts and replies. Extremely-dry writing style. Oftentimes almost zero math or even math symbol to be seen. Uses some proprietary model with specific versions. Seems like a lot of work to (even want to) try to replicate their experiment. Discusses very subjective (and boring, at least to me) matters such as LLM emotions or introspections. Who are these papers even written for? Certainly nobody is sitting down to read 100+ of subjective interpretations for a model that's barely accessible to the public, right? There are assigned readings for highschool english classes that are shorter than these papers. It seems to be a huge effort now to even check one of these papers for correctness or to formulate some thoughts around the paper. Just very confused at the state of LLM research. submitted by /u/NeighborhoodFatCat [link] [Kommentare]

I’ve been working on a project called MemoryOps AI. The problem I’m trying to solve is context debt in AI agents. Most memory demos look like this: chat message → vector database → retrieve later That works for demos, but I think production agents need more than retrieval. They need rules for what memory is allowed to survive, what should expire, what should be blocked, what can be updated, and what must be audited. MemoryOps AI treats memory as governed state. The lifecycle is: Capture → Evaluate → Store → Retrieve → Rank → Compose → Update → Forget → Audit Some things I built into it: Policy-before-storage, so sensitive/secret-like content is filtered before memory is saved Typed memories instead of one generic memory bucket Tenant isolation Deletion guarantees Provenance for stored memory Append-only audit logs Retention policies Legal hold Consent-aware memory Background workers for lifecycle tasks A small playground/demo to test memory behavior I’m not posting this as a polished company launch. I’m mainly looking for feedback from people building agents, RAG systems, evals, or AI infrastructure. The questions I’m trying to answer are: What should an AI memory system be allowed to remember? How should old memory expire or get overwritten? How would you test that deleted memory never influences future output? What invariants would you expect before trusting memory in a real assistant? GitHub: https://github.com/patibandlavenkatamanideep/memoryops-ai Demo: memoryops-ai-production.up.railway.app Would appreciate any technical feedback, especially around memory lifecycle design, governance, and evals. submitted by /u/Fit_Fortune953 [link] [Kommentare]

I have building alternative ways explore scientifc literature. The goal was to make the large number of papers published daily easier to keep up with by visualising the macro scopic trend. It is free to use at The Global Research Space for any one interested in giving it a try! How I built it I sourced the latest 11M papers from OpenAlex and Arxiv and ecoded them using SPECTER 2 on titles and abstracts then projecting it down to 2d using UMAP and creating labels within voronoi bounds around high density peaks at increasingly deep depths. There is also support for both keyword and semantic queries, and there's an analytics layer for ranking institutions, authors, and topics etc. I have also more recently added to ability to slide back and forth in time and a daily auto ingestion script to ensure the map is up to date. Feedback or suggestions is very welcome! submitted by /u/icannotchangethename [link] [Kommentare]

Hi everyone, Posted this a while back... a checklist I made while prepping for a CV internship (landed it, hence sharing). It's not a textbook, just a phase-by-phase map of what to actually study for CV/ML interviews: math → CNNs → ViTs → detection → tracking, plus specialization tracks you pick based on the role. After checking on it after a while it got a decent number of stars which surprised and made me happy that people found it useful to save it for later. I decided after that to add more in-demand tracks to help more people after doing some research of the basic internship requirements and maybe a little more. So, just added three new specialization tracks: Segmentation, OCR, and VLMs, on top of the existing ReID and Deployment tracks. Also cleaned up the structure a bit and added proper contributing guidelines if anyone wants to add their own track (3D vision, pose estimation, etc. are open). GitHub: https://github.com/David-Magdy/CVIL Feedback/PRs welcome, especially if something's outdated or miscategorized. And remember to keep it CVIL! submitted by /u/PolarIceBear_ [link] [Kommentare]

EACL 2027 just published their CFP which contains an important change to the common ARR process: For this cycle, author response and author-reviewer discussion are two separate stages Looking at the deadlines, they not only split the process but also allow more time: Author response period Sept 14-19, 2026 Reviewer engagement and Author-reviewer discussion Sept 20-24, 2026 Previously, ARR cycles only gave five days in total for the discussion period. ARR May 2026, for example, only gives July 7 to July 13 for the total authors-reviewer discussion (no separate author response period). In summary, that means not only that the process is being split in two stages but you now also have more time. --- In my opinion this is really good as in the past having just 5 days to post a reply (potentially involving new experiments - even though that is not the original idea of the discussion period) and getting into a discussion with the reviewers felt very tight - for authors and reviewers. I am, therefore, really looking forward to this change. Any thoughts? submitted by /u/S4M22 [link] [Kommentare]

Need to build a ml model to find the price elasticity at the product group level first the given price and discount. What are features I need have and which model used in the industry for these type of use cases . I have used regression and random regression to predict the qty sold. Looking for help on this submitted by /u/batmanparam [link] [Kommentare]

In Wu et. al, the MLE objective is computationally infeasible due to the high number of images in the dataset. Non-parametric Softmax Negative Log-Likelihood With large n, the denominator in (2) is hard to compute. Therefore, they use NCE (Noise-Contrastive Estimation). The NCE Objective Essentially, they approximate the difficult loss in (3) with the easier to compute loss in (7). However, we end up estimating the denominator anyways in (8). Why not just approximate the denominator in (2) with (8)? I asked Claude about this and it said something about it being a biased estimator, but I didn't really get that. I'm also a little confused on the connection of the original NCE formulation as being a way to estimate density and the way it is used here; do we do this because NCE loss is easier to compute and as m (the number of noise samples) increases, we get the gradients of NCE loss and gradients of NLL loss to match? submitted by /u/No_Balance_9777 [link] [Kommentare]

Premise: this work is my first year PhD, and I dropped out for personal reasons. I still want to do research but independently. I have tried to submit my explainability paper to MICCAI. Sadly, for doubtful/good reasons, it got rejected. Among the reviewers, one explicitly suggested to make it stronger and that the work is "novel". I was wondering if a good strategy would be to work on it more (maybe improving also the time it takes for doing experiments, since currently it's a way too big model) and then submitting it to a journal, or first submitting to a workshop and then extend the research for a journal publication. Strategically wise, is it good to first workshop and then journal? MLCN/iMIMIC would be my choices. But I hear a lot about workshop being suboptimal. Given I am not currently optimising for a PhD, does it make sense to go for the long run and publish it as a journal paper/another conference? Thank you in advance. submitted by /u/KingPowa [link] [Kommentare]

(this was deleted before but i dont know if it was the filters of reddit or the moderators, if is the moderators i will not post it again after you delete it sorry.) (The name will probably change soon because I didn't realize "AgroVision" is already a registered trademark lol.) Link: https://agrovision10.vercel.app/ AgroVision DEMO is a personal project that started as a university assignment. It attempts to propose a solution to a real problem in Nicaragua: crop loss caused by misinformation or difficulty accessing useful agricultural information. The traditional methods Nicaraguan farmers use are gradually becoming less accurate due to global warming, and the rise of artificial intelligence opens up new and interesting possibilities. What is it? AgroVision is a free demo that aims to help small and medium-scale producers in Nicaragua decide what crop to plant, when, and with which inputs — by simulating the future climate of their area and calculating whether it's worth it or not, in real córdobas. In general terms, AgroVision is an expert system that lets you "simulate" having a farm. You have your supplies, your available crops to plant, and your plots with their respective active or passive tools. A passive tool would be a specific mesh netting, and an active one would be an irrigation system that activates when needed. You also define your plot's soil type, terrain slope, the year you want to plant, and the specific municipality — we have all of them in Nicaragua. The system has information on each available crop: its growth phases, when planting begins in Nicaragua's 3 main agricultural cycles (primera, postrera, apante), when each cycle ends, water requirements in mm per phase, and most importantly, what climate conditions are ideal for that crop. With this, the system knows what climate conditions to expect for each future day in your area. With all that information, the system simulates what would happen if you planted a certain crop on that plot: which losses are unavoidable due to climate, which are avoidable if you have certain tools or supplies, and finally gives you the result in money generated, quintals produced, and much more. You can even change the sale price per quintal if you want to explore hypothetical scenarios. How did we build it? First comes the NASA data. Using machine learning — essentially specialized math applied to computers to find patterns in phenomena — I obtained daily climate data in 50×50 kilometer grids covering every part of Nicaragua. Being 50×50 km grids, the information is moderately precise. For comparison: weather apps on your phone typically use 20×20 km grids for rainfall, which allows them to predict rain hours in advance. AgroVision's data is more general for now, which works fine for some variables but could improve for others like rainfall, depending on data and resources we obtain in the future. That said, we do provide more precise solutions for variables that require it, like soil moisture at the root level. The variables obtained from NASA are: PRECTOTCORR: Rainfall (mm per day) T2M_MAX / T2M_MIN / T2M: Maximum, Minimum and Average Temperature (°C) WS2M: Wind speed (m/s) RH2M: Relative Humidity (%) ALLSKY_SFC_PAR_TOT: Photosynthetically Active Radiation (W/m²) ALLSKY_SFC_SW_DIFF: Diffuse Radiation (W/m²) GWETTOP / GWETROOT / GWETPROF: Surface, Root Zone and Deep Soil Moisture (fraction 0 to 1) T2MDEW: Dew Point (°C) TS: Soil Temperature (°C) BRECHA_ROCIO: Dew Gap, calculated as T2M_MIN minus T2MDEW (°C) This data was collected daily from 2010 to 2025. Then, using machine learning, I trained a model that learns the mathematical patterns of each variable and uses them to predict future years. We now have these variables projected for 2026–2029. In Nicaragua these variables are especially erratic, which makes this problem particularly interesting. In the graphics section of the site you can see how those predictions turn out. The model successfully captures general patterns, but extreme events like very strong storms or specific natural phenomena can't be detected realistically with this approach — that requires different data, engineering, and resources. The government already handles this with specialized techniques and a different approach, focused on informing days or weeks in advance. The pillars of the simulation engine Even though I'm not an agronomist, the system is built on real scientific principles: 1. Yield Gap Analysis The plant starts with the potential to produce 100% of its harvest. The system assumes that maximum from the start and subtracts percentages when climate causes losses that couldn't be countered. It's more realistic for predicting damage than trying to "add up" growth day by day. 2. Stateful Bucket Model The soil works like a sponge with two layers: the surface, which fills quickly with rain but evaporates fast, and the root zone, which fills more slowly but retains water longer. If it poured on Tuesday, the sponge is full. If Thursday and Friday are sunny with no rain, the system doesn't cry "drought!" — it checks its virtual sponge and says "relax, the roots still have water from Tuesday." This replaces generic NASA data with a plot-specific microclimate for variables that directly affect the plant. 3. Phenological Thresholds Climate doesn't affect a newly germinated plant the same way it affects one in flowering. The engine evaluates each climate event according to the exact growth phase of the crop. 4. Strict Climate Synergy Pests and fungi don't appear out of nowhere — they need the exact combination of conditions. For example: "If humidity is >85% AND temperature is