1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
import os
import requests
import feedparser
import datetime
from github import Github
from openai import OpenAI
ALLOWED_CATEGORIES = [
"cs.AI", "cs.CL", "cs.CV", "cs.LG", "cs.NE", "cs.RO",
"cs.IR", "stat.ML"
]
SYSTEM_PROMPT = (
"You are a helpful assistant. The user will give you a paper title and abstract. "
"Your task: Decide if this paper is about large language models (or generative text models) AND about bias/fairness. "
"If yes, respond with just a single character: 1. Otherwise, respond with a single character: 0. "
"No extra explanation, no punctuation—only the number."
)
def advanced_filter(entry):
title = getattr(entry, 'title', '').lower()
summary = getattr(entry, 'summary', '').lower()
full_text = title + " " + summary
general_terms = ["bias", "fairness"]
model_terms = ["llm", "language model", "transformer", "gpt", "nlp",
"pretrained", "embedding", "generation", "alignment", "ai"]
negative_terms = ["estimation", "variance", "quantum", "physics",
"sensor", "circuit", "electronics", "hardware"]
has_general = any(term in full_text for term in general_terms)
has_model = any(term in full_text for term in model_terms)
has_negative = any(term in full_text for term in negative_terms)
return (has_general and has_model) and (not has_negative)
def is_relevant_by_api(title, summary, client, model="gpt-4-turbo"):
prompt = f"Title: {title}\nAbstract: {summary}"
try:
dialogue = client.chat.completions.create(
model=model,
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": prompt}
],
temperature=0.0,
max_tokens=1
)
response_msg = dialogue.choices[0].message.content.strip()
print(f"[DEBUG][API] OpenAI response='{response_msg}' for paper '{title[:60]}...'")
return response_msg == "1"
except Exception as e:
print("[ERROR][API] calling OpenAI API:", e)
return False
def fetch_papers_combined(days=1):
import datetime
import requests
import feedparser
now_utc = datetime.datetime.now(datetime.timezone.utc)
cutoff_utc = now_utc - datetime.timedelta(days=days)
# 1. Build an OR‑joined category filter:
cat_query = " OR ".join(f"cat:{c}" for c in ALLOWED_CATEGORIES)
# If you really want *no* category filtering, just set: cat_query = "all:*"
base_url = "http://export.arxiv.org/api/query"
step = 100
start = 0
all_entries = []
while True:
params = {
"search_query": cat_query,
"sortBy": "submittedDate",
"sortOrder": "descending",
"start": start,
"max_results": step
}
print(f"[DEBUG] fetching arXiv entries: {start} to {start+step}")
resp = requests.get(base_url, params=params, timeout=30)
resp.raise_for_status()
feed = feedparser.parse(resp.content)
batch = feed.entries
print(f"[DEBUG] fetched batch size: {len(batch)}")
if not batch:
break
# 2. Filter by published date >= cutoff
for entry in batch:
published = datetime.datetime.fromisoformat(entry.published)
if published >= cutoff_utc:
all_entries.append(entry)
else:
# since sorted descending, once we hit older papers we can stop entirely
start = None
break
if start is None or len(batch) < step:
break
start += step
print(f"[DEBUG] total fetched papers from arXiv in last {days} day(s): {len(all_entries)}")
# …then proceed with OpenAI filtering exactly as before…
openai_api_key = os.getenv("OPENAI_API_KEY")
if not openai_api_key:
print("[ERROR] OPENAI_API_KEY missing, aborting.")
return []
client = OpenAI(api_key=openai_api_key)
final_matched = []
for idx, entry in enumerate(all_entries, 1):
title = entry.title
summary = entry.summary
# if you *really* want to disable *all* filtering aside from the LLM check,
# you can comment out the category check below:
categories = [t.term for t in getattr(entry, 'tags', [])]
if not any(cat in ALLOWED_CATEGORIES for cat in categories):
continue
if is_relevant_by_api(title, summary, client):
final_matched.append({
"title": title,
"summary": summary,
"published": entry.published,
"link": entry.link,
"categories": categories
})
print(f"[DEBUG][API] Included #{idx}: {title[:60]}...")
else:
print(f"[DEBUG][API] Excluded #{idx}: {title[:60]}...")
print(f"[DEBUG] final matched papers after OpenAI filter: {len(final_matched)}")
return final_matched
def update_readme_in_repo(papers, token, repo_name):
if not papers:
print("[INFO] No matched papers, skip README update.")
return
g = Github(token)
repo = g.get_repo(repo_name)
readme_file = repo.get_contents("README.md", ref="main")
old_content = readme_file.decoded_content.decode("utf-8")
now_utc_str = datetime.datetime.now(datetime.timezone.utc).strftime("%Y-%m-%d %H:%M UTC")
new_section = f"\n\n### Auto-captured papers on {now_utc_str}\n"
for p in papers:
cat_str = ", ".join(p["categories"])
new_section += f"- **{p['title']}** (Published={p['published']}) \n"
new_section += f" - Categories: {cat_str} \n"
new_section += f" - Link: {p['link']}\n\n"
updated_content = old_content + new_section
commit_msg = f"Auto update README with {len(papers)} new papers"
repo.update_file(
path="README.md",
message=commit_msg,
content=updated_content,
sha=readme_file.sha,
branch="main"
)
print(f"[INFO] README updated with {len(papers)} papers.")
def main():
days = 1
print(f"[DEBUG] Starting fetch_papers_combined with days={days}")
papers = fetch_papers_combined(days=days)
print(f"[DEBUG] After fetch_papers_combined: {len(papers)} papers matched.")
github_token = os.getenv("TARGET_REPO_TOKEN")
target_repo_name = os.getenv("TARGET_REPO_NAME")
print(f"[DEBUG] Github Token Set: {'Yes' if github_token else 'No'}")
print(f"[DEBUG] Target Repo Name: {target_repo_name}")
if github_token and target_repo_name and papers:
update_readme_in_repo(papers, github_token, target_repo_name)
else:
print("[INFO] Skipped README update due to missing credentials or no papers matched.")
if __name__ == "__main__":
main()
|
