Tootfinder

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Xiaofeng Pan, Jing Chen, Haitong Zhang, Menglin Xing, Jiayi Wei, Xuefeng Mu, Zhongqian Xie
https://arxiv.org/abs/2505.23298

Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Recent works of music representation learning mainly focus on learning acoustic music representations with unlabeled audios or further attempt to acquire multi-modal music representations with scarce annotated audio-text pairs. They either ignore the language semantics or rely on labeled audio datasets that are difficult and expensive to create. Moreover, merely modeling semantic space usually fails to achieve satisfactory performance on music recommendation tasks since the user preference spac…

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
Pengfei Xu, Donggen Wang
https://arxiv.org/abs/2505.23262

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
As a specific domain of subjective well-being, travel satisfaction has attracted much research attention recently. Previous studies primarily use statistical models and, more recently, machine learning models to explore the determinants of travel satisfaction. Both approaches require data from sufficient sample sizes and correct prior statistical assumptions. The emergence of Large Language Models (LLMs) offers a new modeling approach that can overcome the shortcomings of the existing methods. …

LLM-based Property-based Test Generation for Guardrailing Cyber-Physical Systems
Khashayar Etemadi, Marjan Sirjani, Mahshid Helali Moghadam, Per Strandberg, Paul Pettersson
https://arxiv.org/abs/2505.23549

LLM-based Property-based Test Generation for Guardrailing Cyber-Physical Systems
Cyber-physical systems (CPSs) are complex systems that integrate physical, computational, and communication subsystems. The heterogeneous nature of these systems makes their safety assurance challenging. In this paper, we propose a novel automated approach for guardrailing cyber-physical systems using property-based tests (PBTs) generated by Large Language Models (LLMs). Our approach employs an LLM to extract properties from the code and documentation of CPSs. Next, we use the LLM to generate P…

"William Zani, one of the core programmers of the first BASIC compiler, tells the story of the demo of the DTSS system at the San Francisco AFIPS 1964 conference (minute 26:17), sending a BASIC program to from San Francisco to Hanover, New Hampshire over a telephone line, live in front of an audience, who (I quote) “went bananas”."

Dartmouth College
In August 2014, Dartmouth College published a video commemorating the 50th anniversary of the BASIC programming language, the subject of this month's Vidéothèque section. It features original footage from the 1960s and interviews of former students and team members, including Professor Thomas Kurtz, who was 85 years old at the time. But the heart and soul of the video is, without any doubt, Professor John Kemeny himself; not only his technical contributions, which were outstanding by every st…

Linguist Noam Chomsky is interviewed by Common Dreams writer C.J. Polychroniou on the subject of ChatGPT in this May 2023 piece. Chomsky's stance on LLMs is that as long as we're not able to atomically understand what goes on in the statistical black box that currently is an LLM, linguists won't be able to benefit from being able to see whether it learns language like a human does, or not.
"Noam Chomsky Speaks on What ChatGPT Is Really Good For"

Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
Hao Li, Ju Dai, Xin Zhao, Feng Zhou, Junjun Pan, Lei Li
https://arxiv.org/abs/2505.23290

Wav2Sem: Plug-and-Play Audio Semantic Decoupling for 3D Speech-Driven Facial Animation
In 3D speech-driven facial animation generation, existing methods commonly employ pre-trained self-supervised audio models as encoders. However, due to the prevalence of phonetically similar syllables with distinct lip shapes in language, these near-homophone syllables tend to exhibit significant coupling in self-supervised audio feature spaces, leading to the averaging effect in subsequent lip motion generation. To address this issue, this paper proposes a plug-and-play semantic decorrelation …

Let's say you find a really cool forum online that has lots of good advice on it. It's even got a very active community that's happy to answer questions very quickly, and the community seems to have a wealth of knowledge about all sorts of subjects.
You end up visiting this community often, and trusting the advice you get to answer all sorts of everyday questions you might have, which before you might have found answers to using a web search (of course web search is now full of SEI spam and other crap so it's become nearly useless).
Then one day, you ask an innocuous question about medicine, and from this community you get the full homeopathy treatment as your answer. Like, somewhat believable on the face of it, includes lots of citations to reasonable-seeming articles, except that if you know even a tiny bit about chemistry and biology (which thankfully you do), you know that the homoeopathy answers are completely bogus and horribly dangerous (since they offer non-treatments for real diseases). Your opinion of this entire forum suddenly changes. "Oh my God, if they've been homeopathy believers all this time, what other myths have they fed me as facts?"
You stop using the forum for anything, and go back to slogging through SEI crap to answer your everyday questions, because one you realize that this forum is a community that's fundamentally untrustworthy, you realize that the value of getting advice from it on any subject is negative: you knew enough to spot the dangerous homeopathy answer, but you know there might be other such myths that you don't know enough to avoid, and any community willing to go all-in on one myth has shown itself to be capable of going all in on any number of other myths.
...
This has been a parable about large language models.
#AI #LLM

KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W. Lee, Sangdoo Yun, Hyun Oh Song
https://arxiv.org/abs/2505.23416

KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Transformer-based large language models (LLMs) cache context as key-value (KV) pairs during inference. As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency. This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries. KVzip quantifies the importance of a KV pair using the underlying LLM to reconstruct original contexts from cached KV pairs, subsequ…

SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning
Goodluck Archibong, Ardiansyah Koeshidayatullah, Umair Waheed, Weichang Li, Dicky Harishidayat, Motaz Alfarraj
https://arxiv.org/abs/2505.20518

SeisCoDE: 3D Seismic Interpretation Foundation Model with Contrastive Self-Distillation Learning
Seismic interpretation is vital for understanding subsurface structures but remains labor-intensive, subjective, and computationally demanding. While deep learning (DL) offers promise, its success hinges on large, high-quality datasets, often scarce in geophysics. Foundation Models (FMs), which have shown significant success in fields like natural language processing and computer vision, offer a transformative opportunity for seismic interpretation by enabling knowledge transfer and generalizat…

https://youtube.com/watch?v=wfpjNdhpMzg
You know when a foreigner teaches you more about your own country than you know? This guy is such a good subject matter expert at languages, that by accident he knows things about Australian languages and culture that I guarantee you won…