Dhanajit Brahma ✈️ ACL 2026 San Diego

Postdoc, Duke University,
PhD, CSE, IIT Kanpur

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

Huge congrats to Fernando Pereira on the Lifetime Achievement Award at #ACL2026 in San Diego! 🌴🏆
From pioneering Conditional Random Fields (CRFs) to revolutionizing machine learning and NLP, his impact on the field is truly immeasurable.
#NLProc #AI #MachineLearning #CRF

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

8/8
Joint work with Dr. Sijia Wang and Prof. Ricardo Henao at Duke University. 🏛️💙

Feedbacks are welcome — especially from folks running agent memory in production! 💬👇

#LLM #AgenticAI #MachineLearning #AIAgents #MemorySystems #NLP #AgenticMemorySystems

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

1/8
🦉SAGE: A Novelty Gate for Efficient Memory Evolution

tl;dr: Stop wasting compute asking LLMs what to remember. SAGE scores embedding novelty directly! 3.4× cheaper, 2.5× faster, higher accuracy, and 0 LLM routing calls.

📄 https://t.co/ZKoMptT0ck
💻 https://t.co/ilZt0JrE6u

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

7/8
The surprising part: Quality went up, not down. 📈

A principled gate keeps the memory store cleaner, which pays off later on multi-hop & open-domain questions. 🧠✨

Sometimes the answer isn't a bigger model—it's better math at the right bottleneck. 🧮🎯

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

Interested? Let's chat at the session!
🕒 Time: 11:00 AM PST
📍 Spot: Poster #2516
📄 Paper: https://t.co/FfPB1jzV2y
🔗 Details: https://t.co/IOetL4lTx3
#NeurIPS2025 #CausalInference #AIResearch

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

👋 Hello #SanDiego!
We are excited to present our latest work bridging Causal Inference and GenAI at #NeurIPS2025.
For tackling unobserved confounders, come visit on Dec 5, 2025 at 11 AM PST.
📍Poster 2516
Coupling Generative Modeling and an Autoencoder with the Causal Bridge🧵👇

[Dhanajit Brahma ✈️ ACL 2026 San Diego @DhanajitB]

Real-World Impact:
We extended this framework to Survival Analysis (time-to-event). Testing on Framingham Heart Study data, our method demonstrated superior performance compared to SOTA baselines.
Plus, we provide new theoretical bounds on treatment effect errors! ✌️

[Subbarao Kambhampati (కంభంపాటి సుబ్బారావు) @rao2z]

What a tour de force and gracious #NeurIPS2025 Test of Time talk by Kaimeng He! 🙏

Faced with a choice of a "prophet talk" or a "realistic talk", he says he went for the latter, and we are all the richer for it.

"I feel like I am in a ship in Atlantic. Everything ahead is unknown. There is no oracle. No prophet. And when it is discovered, I hope it becomes common knowledge."

[Subbarao Kambhampati (కంభంపాటి సుబ్బారావు) @rao2z]

There is an inherent contradiction in the premise of @zeynep's #NeurIPS2025 talk that I am curious to see how she resolves.. If the humanity was never before been able to figure out the real impact of revolutionary technology, why is it going to be any different this time?

[Chris Barber @chrisbarber]

I made another NeurIPS 2025 hiring list. More teams hiring research engineers, MLEs, SWEs:

@julianibarz at Tesla Optimus
@nlpmattg at @ScaledCognition
@msalbergo at Kempner Institute at Harvard
@chinwei_h at MSFT Research
@apsarathchandar at Chandar Lab
@stash_pomichter at @dimensionalos
@luke_drago_ at @WorkshopLabsPBC
@Yong18850571 at Thinking Machines
@real_ioannis, @MishaLaskin, @FlintCasey & @IrwanBello at Reflection AI
@mgalle at Cohere
@daylenyang at Waymo
@ok_ikaros at @spellbrush
@apjacob03, @EugeneVinitsky, @ChristosTzamos, @justintchiu & @zzzzgq at Percepta
@zw_zachwang & @maxwshen at Genentech
@vishrav at MSL
@ryan_kidd44 at @MATSprogram
@grx_xce at Design Arena
@preslav_nakov at MBZUAI
@BhavinJawade at Netflix
@JiarenCai & @olive_jy_song at MiniMax
@jefffhj & @LiangchenLuo at xAI
@KrishnaPillutla at IIT Madras
@ravi_sola & @DonalByrne2 at @PrimaMente
@jocelynqchen at U of Alberta
@serenaa_ge at @datacurve
@brianryhuang & @thenickmoy at Antigravity, Google DeepMind
@junze_liu at Amazon
@amir_habibian at Qualcomm
@ismaililkanc at AITHYRA & TU Vienna
@MardaniMorteza at Nvidia

[Chris Barber @chrisbarber]

I made an unofficial NeurIPS 2025 hiring list:

@rronak_, @QuantumArjun, @michaelelabd, stealth, I’m a small investor: RL post-training from live product usage. Research Engineers.
@jonsid, Turing: data for frontier models. Research Engineers, SWEs.
@schwarzjn_, ICL & Thomson Reuters: LLMs for law. Research Engineers, SWEs, PhD students.
@panda_liyin, AdaL: copilot for ML engineering. MLEs, SWEs.
@sarwal_varuni, TriFetch: data and post-training for medical AI.
@bidhan, Bagel Labs: decentralized training for diffusion models. MLEs, ML Scientists.
@meggmcnulty, Cosmic Labs: AI-native OS for embedded engineering. MLEs, SWEs, systems engineers.
@samuelekpe, GrupaAI: operating system for AI agents. SWEs.
@jaradcannon, Humanoid: industrial humanoids. SWEs and applied researchers.
@saurabh_here1, Cantina: AI native social media. Research interns for video gen.
@RicardoMonti9, DatologyAI: frontier data curation (filtering, mixing, synthetic) for LLMs. Research Scientists, MLEs, SWEs.
@NimaGard, Path Robotics: physical AI to automate manufacturing tasks (e.g. welding). MLEs for robot learning.
@DrJimFan, Nvidia robotics team. Research Engineers, SWEs.
@katherine1ee, OpenAI pretraining safety team. Research Engineers.
@BorisMPower, OpenAI applied AI research team. Research Engineers.
@j_asminewang, OpenAI alignment team. Research Engineers, Research Scientists.
@zijianwang30, MSL data research team. Research Engineers, Research Scientists.
@RuiqiGao, Google DeepMind video gen team. Research Engineers, Research Scientists.
@joshim5, Chai Discovery: molecule prediction for drug discovery. Research Engineers, SWEs.
@crisbodnar, Project Prometheus: AI for manufacturing and logistics. Research Engineers.
@vdbergrianne, Microsoft Research Amsterdam materials science team. Research Engineers.
@kamath_sutra, Smallest: AI for call centers. SWEs.
@idavidrein, METR: frontier model evaluation. Research Engineer.
@jimmysmith1919, Liquid AI: on-device models. MLEs, Research Engineers.
@alxndrdavies, AI Security Institute: red-teaming. Research Scientists/Engineers.
@stuhlmueller, Elicit: AI for scientific research and good reasoning. MLEs, SWEs.
@gavincrooks, @FarisSbahi, Normal Computing: physics-based ASICs. Research Engineers, SWEs.
@myra_deng, Goodfire AI: interpretability research. Research Engineers, Research Scientists, MLEs.
@_lychrel, @SergeiIakhnin, @ja_kirkpatrick, @sbos, Isomorphic Labs: AI-first drug discovery. Research Engineers, Research Scientists, MLEs.
@kdqg1, @bneyshabur, Anthropic AI Scientist team. Research Engineers with infra experience.
@sarahookr, Adaption: continuous learning. Research Engineers.
@francedot, Cua, I’m a small investor: infra for computer-use agents. SWEs, Research Engineers.
@iScienceLuvr, Sophont: multimodal models for healthcare. Research Engineers/Research Scientists.
@aditshah00, Until Labs: organ preservation. MLEs.
@RitvikKapila & @gauri__gupta, NeoSigma: evals and post-training for real world agents. SWEs.
@abeirami, stealth: reliability & statistical evaluation. Research Engineers & SWEs.
@adityachinchure, Ideogram: image generation. Research Engineers.
@AndrewLBeam, @kenneth0stanley, Lila Sciences: autonomous labs, verifiability for science. Research Engineers, MLEs.
@brianwilt, Waymo: ML infra for motion planning team. Senior SWEs.
@thisismadani, Profluent Bio: protein generation for drug development. MLEs.

[Raghuveer T @raghavlite]

📢 New preprint announcement!!

- Most image encoders are trained independently before being integrated into a VLM, resulting to generic, query-agnostic image representations that constrain downstream VLM performance.

- 🚀 We introduce the Text-Guided Semantic Image Encoder (TIE), which produces query/image-conditioned image representations, enabling VLMs to operate on task-relevant image features.

- Across nine image-to-text benchmarks, TIE-based VLMs achieve +1.5–1.3 average improvements, with gains of up to +6 points on DocVQA and InfoVQA.

- Notably, despite using only half the image tokens, and therefore offering faster inference, TIE-based VLMs outperform their standard counterparts.

Paper: https://t.co/MS1ZPg1A4a

[DukeNLP @duke_nlp]

Our entire group has come to San Diego this week and we have a series of presentations at #NeurIPS! Come find us and chat about contrastive learning and LLM agents.
@bhuwandhingra @raghavlite @syz0x1 @JunlinWang3 @RoyXie_ @DCirci