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.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
1
49
-
1
61
150
h
u
m
a
n
i
n
t
e
r
v
e
n
t
i
o
n
[
5
]
–
[
7
]
.
T
h
i
s
n
e
c
es
s
it
y
l
e
a
d
s
t
o
i
n
c
r
e
as
ed
o
p
e
r
a
t
i
o
n
a
l
c
o
s
ts
,
r
e
d
u
c
e
d
s
p
e
e
d
,
a
n
d
h
i
n
d
e
r
s
t
h
e
u
b
i
q
u
i
t
y
o
f
R
PA
w
h
i
l
e
a
ls
o
i
n
cr
e
a
s
i
n
g
t
h
e
h
es
i
ta
n
c
y
i
n
a
d
o
p
t
io
n
.
Ag
en
tic
AI
,
a
n
d
a
g
en
tic
lar
g
e
lan
g
u
ag
e
m
o
d
els
(
L
L
Ms
)
in
p
ar
ticu
lar
,
h
av
e
g
ain
ed
im
m
en
s
e
p
o
p
u
lar
ity
f
o
r
th
ei
r
n
ea
r
-
h
u
m
an
ab
ilit
ies
in
p
er
f
o
r
m
i
n
g
r
ea
l
wo
r
ld
task
s
,
in
clu
d
in
g
i
n
ter
ac
tin
g
with
UI
.
L
ev
er
ag
in
g
r
o
b
u
s
t
ze
r
o
-
s
h
o
t
lear
n
in
g
ca
p
ab
ilit
ies,
th
ese
m
o
d
els
—
ex
em
p
lifie
d
b
y
I
B
M
Gr
an
ite
an
d
Mic
r
o
s
o
f
t
’
s
Au
to
g
en
—
ef
f
ec
ti
v
ely
g
en
er
alize
f
r
o
m
m
in
im
al
in
p
u
t
to
ex
ec
u
te
co
m
p
lex
o
p
er
atio
n
s
ac
r
o
s
s
d
iv
er
s
e
d
o
m
ai
n
s
[
8
]
–
[
1
0
]
.
B
u
ilt
o
n
s
tate
-
of
-
t
h
e
-
ar
t
tr
a
n
s
f
o
r
m
er
a
r
ch
itectu
r
es
an
d
u
n
s
u
p
er
v
is
ed
lear
n
in
g
p
ar
ad
ig
m
s
,
th
ey
en
ab
le
au
to
n
o
m
o
u
s
d
ec
is
io
n
-
m
ak
in
g
a
n
d
s
tr
ea
m
lin
ed
u
s
er
in
ter
f
ac
e
in
te
r
ac
tio
n
s
.
Ho
wev
er
,
th
ese
p
er
f
o
r
m
an
ce
g
ain
s
co
m
e
at
th
e
ex
p
en
s
e
o
f
s
ig
n
if
ican
t
co
m
p
u
tatio
n
al
o
v
er
h
ea
d
,
lead
in
g
to
h
ig
h
e
n
er
g
y
co
n
s
u
m
p
tio
n
[
1
1
]
,
[
1
2
]
,
in
cr
ea
s
ed
laten
cy
[
1
3
]
,
a
n
d
elev
ate
d
o
p
er
atio
n
al
c
o
s
ts
.
On
g
o
in
g
r
esear
ch
co
n
ti
n
u
es
to
f
o
cu
s
o
n
m
itig
atin
g
th
ese
d
r
a
wb
ac
k
s
wh
ile
f
u
r
th
er
r
ef
in
i
n
g
th
eir
f
u
n
ctio
n
al
ca
p
ab
ilit
ies.
T
h
is
p
ap
er
p
r
o
p
o
s
es
th
e
u
s
e
o
f
L
L
Ms
th
r
o
u
g
h
t
h
e
Gr
ap
h
R
AG
[
1
4
]
f
r
am
ewo
r
k
as
an
in
t
er
m
ed
iar
y
s
u
p
er
v
is
o
r
.
T
h
is
wo
r
k
s
tr
id
es
to
war
d
s
h
y
p
er
a
u
to
m
atio
n
[
1
5
]
an
d
u
n
ass
is
ted
R
P
A.
Pre
v
io
u
s
ly
,
J
ain
et
a
l
.
[
1
6
]
p
r
o
p
o
s
ed
th
e
u
s
e
o
f
L
L
Ms f
o
r
R
PA f
o
r
f
o
r
m
f
illi
n
g
.
Gr
ap
h
R
AG
is
a
n
o
v
el
f
r
am
e
wo
r
k
b
u
ilt
to
p
r
esen
t
lar
g
e
a
m
o
u
n
ts
o
f
co
n
tex
t
u
al
in
f
o
r
m
atio
n
to
an
L
L
M.
I
n
th
e
Gr
ap
h
R
AG
g
lo
b
al
s
ea
r
ch
p
r
o
ce
s
s
[
1
4
]
,
[
1
6
]
,
t
h
e
L
L
M
co
n
f
er
s
a
k
n
o
wled
g
e
g
r
ap
h
to
p
r
o
d
u
ce
an
o
u
tp
u
t
to
a
n
y
u
s
er
q
u
er
y
.
W
e
p
r
o
p
o
s
e
r
e
p
r
esen
tin
g
th
e
R
PA
wo
r
k
f
lo
w
p
r
o
ce
s
s
,
em
b
ed
d
ed
with
o
p
er
atio
n
al
in
f
o
r
m
atio
n
,
as
a
g
r
ap
h
to
b
e
q
u
er
ied
b
y
th
e
L
L
M
in
a
p
r
o
ce
s
s
s
im
ilar
to
th
e
af
o
r
em
en
ti
o
n
ed
g
lo
b
al
q
u
er
y
,
en
ab
lin
g
a
m
o
r
e
d
etailed
an
aly
s
is
o
f
th
e
b
o
t
’
s
wo
r
k
f
lo
w.
W
e
p
r
o
v
id
e
a
g
en
er
alize
d
p
i
p
elin
e
with
h
eu
r
is
tic
-
d
r
iv
e
n
ex
p
ed
ien
t
L
L
M
ca
lls
ap
p
licab
le
ac
r
o
s
s
d
o
m
ain
s
an
d
task
s
.
T
h
e
id
ea
o
f
ex
p
ed
ien
tly
ca
llin
g
L
L
Ms
—
i.e
.
,
o
n
ly
wh
en
an
e
x
ce
p
tio
n
o
cc
u
r
s
an
d
th
e
R
PA
is
p
r
o
g
r
ess
in
g
alo
n
g
an
in
c
o
r
r
ec
t
p
ath
—
was
in
s
p
ir
ed
b
y
o
th
er
s
o
f
twar
e
en
g
in
ee
r
i
n
g
r
esear
ch
s
o
lu
tio
n
s
in
clu
d
in
g
[
1
7
]
.
T
h
e
ex
p
ed
ie
n
t
ca
llin
g
o
f
L
L
Ms
o
f
f
er
s
v
ar
io
u
s
b
en
ef
its
in
clu
d
in
g
m
in
im
iz
in
g
co
m
p
u
tatio
n
al
co
s
ts
an
d
h
en
ce
,
en
er
g
y
u
s
a
g
e,
with
th
e
en
v
ir
o
n
m
en
tal
b
en
ef
its
b
ein
g
s
ig
n
if
ican
tly
p
r
o
n
o
u
n
ce
d
at
s
ca
le;
in
cr
ea
s
ed
r
esil
ien
ce
to
ch
a
n
g
es
in
th
e
u
n
d
er
ly
in
g
p
r
o
ce
s
s
as
n
ee
d
ed
;
a
n
d
r
e
d
u
ctio
n
i
n
p
r
o
ce
s
s
in
g
tim
e
co
m
p
ar
ed
to
r
e
p
lacin
g
th
e
R
PA b
o
t w
ith
an
L
L
M.
2.
L
I
T
E
R
AT
U
RE
SU
RVE
Y
R
PA
h
as
ev
o
lv
ed
r
ap
id
l
y
o
v
er
th
e
p
ast
d
ec
a
d
e,
b
ec
o
m
in
g
a
co
r
n
e
r
s
to
n
e
tech
n
o
lo
g
y
in
in
d
u
s
tr
ies
s
u
ch
as
b
a
n
k
in
g
,
in
s
u
r
an
ce
,
an
d
m
a
n
u
f
ac
t
u
r
in
g
[
1
]
–
[
3
]
.
E
ar
ly
s
tu
d
ies
p
r
im
ar
ily
f
o
cu
s
e
d
o
n
th
e
ef
f
icien
c
y
g
ain
s
an
d
co
s
t
r
ed
u
ctio
n
s
ac
h
i
ev
ab
le
th
r
o
u
g
h
R
PA.
Ho
wev
er
,
as
p
r
ac
tical
d
ep
lo
y
m
en
ts
ex
p
an
d
ed
,
r
esear
ch
er
s
b
eg
an
to
h
ig
h
lig
h
t
a
cr
itical
s
h
o
r
tco
m
in
g
:
tr
a
d
itio
n
al
R
PA
s
y
s
tem
s
s
tr
u
g
g
le
with
e
x
ce
p
ti
o
n
h
an
d
lin
g
wh
e
n
co
n
f
r
o
n
ted
with
d
y
n
am
ic
a
n
d
u
n
f
o
r
eseen
s
ce
n
ar
io
s
[
4
]
–
[
6
]
.
A
s
ig
n
if
ican
t p
o
r
tio
n
o
f
th
e
lit
er
atu
r
e
u
n
d
er
s
co
r
es th
e
ch
alle
n
g
es p
o
s
ed
b
y
ex
ce
p
tio
n
s
—
r
an
g
in
g
f
r
o
m
u
n
ex
p
ec
te
d
u
s
er
in
ter
f
ac
e
ch
a
n
g
es
to
v
ar
iatio
n
s
in
b
u
s
in
ess
lo
g
ic
—
th
at
ca
n
h
alt
au
to
m
at
ed
wo
r
k
f
lo
ws
an
d
n
ec
ess
itate
co
s
tly
h
u
m
an
in
ter
v
en
tio
n
.
T
h
ese
ch
allen
g
es
h
av
e
s
p
u
r
r
ed
ef
f
o
r
ts
to
in
teg
r
ate
in
tellig
en
t
s
u
p
er
v
is
o
r
y
m
ec
h
a
n
is
m
s
in
to
R
PA
s
y
s
tem
s
.
No
tab
ly
,
r
ec
en
t
r
esear
ch
h
as
p
r
o
p
o
s
ed
lev
er
ag
in
g
a
d
v
an
ce
d
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
tech
n
iq
u
es
to
b
r
id
g
e
th
is
g
a
p
.
J
ain
et
a
l
.
[
1
6
]
an
d
C
h
e
n
et
a
l
.
[
1
7
]
h
a
v
e
d
em
o
n
s
tr
ated
th
at
L
L
Ms
ca
n
b
e
ef
f
ec
tiv
ely
em
p
l
o
y
ed
t
o
b
o
th
d
etec
t
an
d
r
eso
lv
e
e
x
ce
p
tio
n
s
,
th
er
eb
y
m
in
im
izin
g
th
e
n
ee
d
f
o
r
h
u
m
an
o
v
er
s
ig
h
t.
C
o
m
p
lem
en
t
in
g
th
is
ap
p
r
o
ac
h
,
th
e
Gr
ap
h
R
AG
f
r
am
ewo
r
k
in
tr
o
d
u
ce
d
b
y
E
d
g
e
et
a
l
.
[
1
4
]
em
p
lo
y
s
g
r
ap
h
-
b
ased
r
ep
r
es
en
tatio
n
s
o
f
wo
r
k
f
lo
ws
to
en
h
an
ce
th
e
c
o
n
tex
tu
al
u
n
d
er
s
tan
d
i
n
g
o
f
L
L
Ms,
en
ab
l
in
g
m
o
r
e
p
r
ec
is
e
er
r
o
r
lo
ca
liza
tio
n
an
d
r
eso
lu
tio
n
.
W
h
i
l
e
L
L
Ms
o
f
f
e
r
s
i
g
n
i
f
i
c
a
n
t
a
d
v
a
n
t
a
g
e
s
i
n
t
e
r
m
s
o
f
c
o
g
n
i
t
iv
e
c
a
p
a
b
i
li
t
i
es
,
t
h
e
i
r
i
n
t
e
g
r
a
ti
o
n
i
n
t
o
R
PA
w
o
r
k
f
l
o
w
s
r
a
is
es
c
o
n
c
e
r
n
s
r
e
la
t
e
d
t
o
c
o
m
p
u
t
a
t
i
o
n
a
l
o
v
e
r
h
e
ad
a
n
d
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
[
1
8
]
,
[
1
9
]
.
C
o
n
s
e
q
u
e
n
t
l
y
,
t
h
e
l
it
e
r
a
t
u
r
e
e
m
p
h
as
i
z
es
t
h
e
i
m
p
o
r
t
a
n
c
e
o
f
d
e
v
e
l
o
p
i
n
g
s
t
r
a
te
g
i
e
s
f
o
r
t
h
e
e
x
p
e
d
ie
n
t
i
n
v
o
c
a
t
i
o
n
o
f
LLMs
—
e
n
s
u
r
i
n
g
t
h
at
t
h
e
s
e
m
o
d
e
l
s
a
r
e
a
ct
i
v
a
t
e
d
o
n
l
y
w
h
e
n
n
e
c
e
s
s
a
r
y
—
t
o
b
a
la
n
c
e
p
e
r
f
o
r
m
a
n
c
e
w
i
t
h
r
es
o
u
r
c
e
u
t
i
l
i
z
at
i
o
n
.
A
d
d
it
i
o
n
a
ll
y
,
t
h
e
co
n
c
e
p
t
o
f
h
y
p
e
r
a
u
t
o
m
a
ti
o
n
,
as
d
is
c
u
s
s
e
d
i
n
i
n
d
u
s
t
r
y
r
e
p
o
r
t
s
[
1
5
]
,
a
d
v
o
c
a
t
e
s
f
o
r
t
h
e
u
n
i
f
i
c
at
i
o
n
o
f
R
PA
wi
t
h
a
d
v
a
n
c
e
d
A
I
t
ec
h
n
i
q
u
e
s
.
T
h
i
s
i
n
te
g
r
a
t
e
d
a
p
p
r
o
a
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t
p
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i
a
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Fi
g
u
r
e
1
s
h
o
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c
o
m
p
a
r
is
o
n
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o
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T
a
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p
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r
L
L
M
s
a
n
d
r
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t
p
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m
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t
e
r
s
f
o
r
t
h
e
i
r
u
s
e
as
a
g
e
n
ts
.
Ho
wev
er
,
th
e
n
ewly
em
er
g
e
n
t
f
ield
o
f
h
allu
ci
n
atio
n
[
2
0
]
,
[
2
1
]
i
n
L
L
Ms
wh
ich
ar
e
p
r
o
n
o
u
n
ce
d
in
Gr
ap
h
R
AG
[
2
2
]
ev
en
with
th
e
u
s
e
o
f
SOTA
m
o
d
els
lik
e
GPT
-
5
[
2
3
]
.
B
ey
o
n
d
ex
ce
p
tio
n
h
an
d
lin
g
,
r
ec
e
n
t
ad
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an
ce
s
in
ag
e
n
tic
AI
h
av
e
ex
p
an
d
e
d
th
e
ca
p
ab
ilit
ies
o
f
R
PA
s
y
s
tem
s
.
Ag
en
tic
L
L
Ms,
s
u
ch
as
th
o
s
e
d
is
cu
s
s
ed
b
y
Mc
I
n
to
s
h
et
a
l
.
[
2
4
]
a
n
d
B
r
o
h
i
et
a
l
.
[
2
5
]
,
e
m
p
h
asize
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to
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o
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o
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s
d
ec
is
io
n
-
m
ak
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y
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am
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ad
ap
tatio
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,
an
d
m
u
lti
-
s
tep
r
ea
s
o
n
in
g
—
cr
itical
asp
ec
ts
f
o
r
en
s
u
r
in
g
r
o
b
u
s
t
an
d
s
elf
-
h
ea
lin
g
au
to
m
atio
n
.
Ad
d
itio
n
ally
,
th
e
OSW
o
r
ld
b
e
n
ch
m
ar
k
in
tr
o
d
u
ce
d
b
y
Xie
et
a
l
.
[
2
6
]
ev
alu
ates
L
L
M
ag
en
t
s
in
r
ea
l
-
wo
r
l
d
task
ex
ec
u
tio
n
,
h
ig
h
lig
h
ti
n
g
k
e
y
c
h
allen
g
es
in
en
s
u
r
in
g
r
eliab
ili
ty
an
d
ef
f
icien
cy
.
T
h
ese
d
ev
el
o
p
m
en
ts
alig
n
with
Evaluation Warning : The document was created with Spire.PDF for Python.
I
AE
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I
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t
J
R
o
b
&
A
u
to
m
I
SS
N:
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-
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8
6
C
a
s
ca
d
in
g
a
u
to
m
a
ta
to
imp
r
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ve
efficien
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o
f la
r
g
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la
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… (
Hri
s
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.
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151
th
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b
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d
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m
u
lti
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o
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wh
er
e
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L
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p
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g
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ts
o
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e
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s
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h
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,
o
p
tim
ize
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k
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l
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ws,
an
d
im
p
r
o
v
e
o
v
er
all
task
c
o
m
p
letio
n
r
ates
[
2
7
]
,
[
2
8
]
.
Fig
u
r
e
1
.
Sp
ec
tr
u
m
o
f
au
to
m
at
a
p
r
o
p
o
s
ed
with
r
esp
ec
t to
t
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o
ciate
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le
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a
r
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ar
0
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s
h
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t L
L
Ms f
o
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tic
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M
o
d
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C
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A
p
ar
ticu
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r
elev
a
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p
a
r
ad
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in
th
is
co
n
tex
t
is
ze
r
o
-
s
h
o
t
lear
n
in
g
(
Z
SL)
,
wh
ich
en
a
b
le
s
L
L
Ms
to
g
en
er
alize
n
ew
au
to
m
atio
n
s
ce
n
ar
io
s
with
o
u
t ta
s
k
-
s
p
ec
if
ic
r
etr
ain
in
g
[
2
9
]
.
B
r
o
wn
et
a
l
.
[
8
]
d
em
o
n
s
tr
ated
h
o
w
lar
g
e
-
s
ca
le
p
r
e
-
tr
ai
n
ed
m
o
d
e
ls
ca
n
ex
tr
ap
o
late
p
atter
n
s
f
r
o
m
lim
ited
c
o
n
tex
t,
s
ig
n
if
ican
tly
im
p
r
o
v
in
g
ex
ce
p
tio
n
h
an
d
lin
g
in
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
.
Fu
r
th
er
m
o
r
e,
W
an
g
et
a
l
.
[
3
0
]
p
r
o
p
o
s
ed
E
DC
E
W
-
L
L
M,
a
lar
g
e
lan
g
u
ag
e
-
b
ased
ap
p
r
o
ac
h
f
o
r
ef
f
ec
tiv
e
er
r
o
r
d
etec
tio
n
an
d
co
r
r
ec
tio
n
,
s
h
o
win
g
its
p
o
ten
tial in
en
h
an
cin
g
Gr
ap
h
R
AG
-
b
ased
ex
ce
p
tio
n
-
h
an
d
lin
g
m
ec
h
an
is
m
s
.
C
o
llectiv
ely
,
th
ese
s
tu
d
ies
illu
s
tr
ate
a
clea
r
tr
ajec
to
r
y
to
war
d
m
er
g
i
n
g
tr
a
d
itio
n
al
R
PA
s
y
s
tem
s
with
m
o
d
er
n
AI
m
eth
o
d
o
lo
g
ies.
Desp
ite
th
e
p
r
o
g
r
ess
m
ad
e,
a
co
m
p
r
eh
en
s
iv
e
f
r
am
ewo
r
k
th
at
s
ea
m
less
ly
in
teg
r
ates
th
e
s
tr
en
g
th
s
o
f
b
o
th
ap
p
r
o
a
ch
es
wh
ile
m
itig
atin
g
th
eir
i
n
d
iv
id
u
al
lim
itatio
n
s
r
em
ain
s
an
o
p
en
r
esear
c
h
ch
allen
g
e.
T
h
e
wo
r
k
p
r
esen
ted
in
th
is
p
ap
er
aim
s
to
f
ill
th
is
g
ap
b
y
in
tr
o
d
u
cin
g
a
Gr
ap
h
R
AG
-
b
ased
ex
ce
p
tio
n
h
an
d
lin
g
m
ec
h
a
n
is
m
th
at
ex
p
e
d
ites
L
L
M
in
v
o
ca
tio
n
,
th
e
r
eb
y
im
p
r
o
v
in
g
b
o
th
th
e
r
o
b
u
s
tn
ess
an
d
ef
f
icien
cy
o
f
R
PA w
o
r
k
f
lo
ws.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
2
5
8
6
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
,
Vo
l
.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
1
49
-
1
61
152
3.
M
E
T
H
O
D
Ou
r
wo
r
k
f
in
d
s
th
e
b
alan
ce
b
etwe
en
th
e
tem
p
o
r
al
an
d
en
er
g
y
e
f
f
icien
cy
o
f
f
e
r
ed
b
y
R
PA
an
d
th
e
awa
r
en
ess
an
d
k
n
o
wled
g
e
th
at
m
o
d
er
n
lar
g
e
-
s
ca
le
d
ee
p
lear
n
in
g
m
o
d
els s
u
ch
as L
L
Ms o
f
f
er
.
W
e
ac
h
iev
e
th
is
b
y
ca
llin
g
u
p
o
n
L
L
Ms/
v
is
u
al
lan
g
u
ag
e
m
o
d
els
(
VL
Ms)
ex
p
ed
ien
tly
.
Her
e,
we
n
o
te
th
at
th
e
L
L
Ms
p
r
o
p
o
s
ed
ar
e
n
o
t
s
p
ec
ially
tr
ain
e
d
/f
in
e
-
t
u
n
ed
f
o
r
th
e
task
o
f
ex
ce
p
tio
n
h
an
d
lin
g
.
I
n
s
tead
,
we
ex
p
er
im
en
t
with
o
f
f
-
th
e
-
s
h
elf
m
o
d
els
an
d
p
r
o
m
p
t
th
e
m
ap
p
r
o
p
r
iately
to
s
p
ec
if
y
th
e
ir
p
o
s
itio
n
in
th
e
p
ip
elin
e
a
n
d
r
eg
u
late
th
e
f
o
r
m
a
t
o
f
th
eir
o
u
tp
u
t.
T
h
is
ass
u
m
p
tio
n
is
s
u
b
s
tan
tiated
b
y
L
L
Ms b
ein
g
wid
ely
u
s
ed
as z
er
o
-
s
h
o
t
ag
en
ts
[
8
]
.
Sm
all
lan
g
u
ag
e
m
o
d
els
(
SLM
s
)
ca
n
s
er
v
e
as
m
o
r
e
ef
f
icien
t
s
u
p
er
v
is
o
r
s
o
r
ca
n
b
e
ca
s
ca
d
ed
with
L
L
Ms
wh
en
tr
ain
ed
ap
p
r
o
p
r
i
ately
.
Ob
s
tr
u
ctio
n
s
to
th
e
R
PA
wo
r
k
f
lo
w
h
a
v
e
b
ee
n
b
r
o
a
d
ly
ca
teg
o
r
ized
in
t
o
i)
s
y
n
tactic
ex
ce
p
tio
n
s
b
y
i
n
ad
eq
u
ately
p
r
o
g
r
am
m
ed
b
o
ts
(
s
in
ce
ex
ce
p
tio
n
s
ar
e
u
n
p
r
e
d
ictab
le
an
d
o
f
ten
u
n
p
r
ec
e
d
en
ted
)
,
ii)
s
y
n
tactic
e
x
ce
p
tio
n
s
f
r
o
m
u
n
ex
p
ec
ted
an
d
in
co
n
g
r
u
o
u
s
in
p
u
ts
p
r
o
v
id
e
d
,
an
d
iii)
in
co
r
r
ec
t
o
u
tp
u
ts
p
r
o
v
id
e
d
as
a
r
esu
lt
o
f
am
b
ig
u
ity
in
in
p
u
t
f
o
r
m
ats.
I
n
th
e
1
s
t
an
d
2
n
d
ca
s
es,
L
L
M
s
ar
e
ca
lled
u
p
o
n
if
an
d
o
n
ly
if
th
e
wo
r
k
f
lo
w
th
r
o
ws
an
er
r
o
r
,
wh
ile
f
o
r
ca
s
e
3
,
we
u
s
e
a
g
atin
g
m
ec
h
an
is
m
to
d
eter
m
in
e
wh
eth
er
th
e
s
itu
atio
n
war
r
an
ts
an
L
L
M
ca
ll.
Ou
r
wo
r
k
f
lo
w
p
r
o
v
id
es
an
ad
v
an
tag
e
in
en
er
g
y
an
d
tem
p
o
r
al
ef
f
icien
cy
,
s
in
ce
s
er
v
in
g
L
L
Ms
h
as
b
ee
n
a
to
p
ic
o
f
ac
tiv
e
r
esear
ch
[
1
8
]
an
d
in
f
e
r
en
ce
tim
es a
r
e
in
co
m
p
ar
a
b
ly
wo
r
s
e
co
m
p
ar
e
d
to
R
P
A
(
R
PA b
o
t
s
ar
e
a
h
ar
d
co
m
p
u
tin
g
task
)
.
A
d
d
itio
n
ally
,
L
L
Ms
co
n
s
u
m
e
g
ar
g
an
tu
an
am
o
u
n
ts
o
f
en
er
g
y
,
m
ak
in
g
th
eir
u
s
ag
e
f
o
r
R
PA w
o
r
k
f
lo
ws at
lar
g
e
s
c
ales e
n
v
ir
o
n
m
e
n
tally
ir
r
esp
o
n
s
ib
le
[
1
9
]
.
Ho
wev
er
,
L
L
Ms
p
r
esen
t
h
u
m
an
-
lik
e
in
tellig
en
ce
an
d
aw
ar
en
ess
f
o
r
s
p
ec
if
ic
ca
s
es
(
li
k
e
th
e
o
n
es
d
ea
lt
with
in
R
PA
d
o
m
ain
s
)
.
L
L
Ms
p
r
o
v
e
ex
ce
p
tio
n
al
at
r
eso
lv
in
g
r
ea
l
-
wo
r
ld
ex
ce
p
tio
n
s
en
co
u
n
ter
ed
b
y
R
PA
as
d
em
o
n
s
tr
ated
in
o
u
r
r
esu
lts
.
Ou
r
wo
r
k
f
lo
w
p
r
o
v
id
es
a
r
ed
u
ctio
n
in
L
L
M
ca
lls
an
d
a
p
r
o
p
o
r
tio
n
ate
r
ed
u
ctio
n
in
e
n
er
g
y
u
s
ag
e
a
n
d
in
f
er
e
n
ce
tim
es.
T
h
e
r
ed
u
ctio
n
in
u
s
ag
e
is
in
v
er
s
ely
d
ep
en
d
e
n
t
o
n
th
e
co
m
p
lex
ity
o
f
th
e
b
o
t
’
s
wo
r
k
f
l
o
w
am
o
n
g
s
t o
t
h
er
f
ac
to
r
s
.
R
PA
wo
r
k
f
lo
w
ca
n
b
e
r
ep
r
esen
ted
as
a
f
ea
tu
r
e
-
r
ic
h
d
ir
ec
t
ed
g
r
ap
h
.
Ma
n
y
R
PA
d
esig
n
s
o
f
twar
e
,
s
u
ch
as
UiPath
,
o
f
f
er
b
u
ilt
-
in
to
o
ls
to
c
o
n
v
er
t
th
e
wo
r
k
f
lo
w
to
a
J
SON
f
ile.
As
a
p
ar
t
o
f
t
h
e
p
r
e
-
p
r
o
ce
s
s
in
g
,
th
e
J
SON
f
ile
i
s
co
n
v
er
ted
in
to
a
g
r
ap
h
wh
ich
s
er
v
es
as
t
h
e
in
p
u
t
to
Gr
ap
h
R
AG.
Gr
ap
h
R
AG
en
ab
les
th
e
h
an
d
lin
g
o
f
co
m
p
lex
wo
r
k
f
lo
ws
an
d
allo
ws
f
o
r
s
tr
u
ctu
r
ed
an
aly
s
is
wh
en
an
er
r
o
r
o
cc
u
r
s
b
y
co
n
s
tr
u
ctin
g
a
tr
ee
o
f
in
f
o
r
m
atio
n
u
p
o
n
th
e
g
r
ap
h
o
f
th
e
wo
r
k
f
lo
w.
Fig
u
r
e
2
d
ep
icts
th
e
p
r
o
p
o
s
ed
p
ip
elin
e
f
o
r
ex
ce
p
tio
n
h
a
n
d
lin
g
.
W
h
en
an
R
PA
b
o
t
e
n
c
o
u
n
ter
s
an
er
r
o
r
,
a
n
L
L
M
is
ca
lled
u
p
o
n
p
r
io
r
to
h
u
m
a
n
in
ter
v
e
n
tio
n
.
F
ir
s
t,
th
e
in
ter
m
ed
iate
o
u
t
p
u
ts
o
f
th
e
v
ar
io
u
s
b
lo
ck
s
o
f
th
e
wo
r
k
f
lo
w
(
s
u
ch
as
co
n
d
itio
n
al
s
tatem
en
ts
an
d
iter
ab
le
s
)
ar
e
im
p
u
ted
in
to
th
e
p
r
e
v
io
u
s
ly
p
r
e
p
ar
ed
g
r
a
p
h
o
f
th
e
w
o
r
k
f
l
o
w.
T
h
e
n
,
we
p
r
ep
ar
e
a
p
r
o
m
p
t
b
y
co
m
b
in
in
g
:
th
e
er
r
o
r
m
ess
ag
e
+
th
e
b
lo
ck
d
u
r
in
g
wh
o
s
e
ex
ec
u
tio
n
th
e
e
r
r
o
r
o
cc
u
r
r
e
d
+
a
p
r
ed
ef
i
n
ed
p
r
o
m
p
t
f
o
r
o
u
r
p
ip
elin
e
.
T
h
e
p
r
o
m
p
ts
wer
e
tailo
r
ed
to
e
n
s
u
r
e
p
r
ec
is
e
an
d
co
n
tex
tu
al
r
esp
o
n
s
es
r
ath
er
th
an
g
en
er
ic
tr
o
u
b
l
esh
o
o
tin
g
s
tep
s
.
B
elo
w
ar
e
th
e
p
r
o
m
p
ts
u
s
ed
f
o
r
th
e
r
esp
ec
tiv
e
u
s
e
ca
s
es
.
Fig
u
r
e
2
.
E
x
ce
p
tio
n
h
an
d
lin
g
p
ip
elin
e
3
.
1
.
E
nh
a
ncing
do
cum
ent
v
a
lid
a
t
io
n
in K
YC
T
h
e
p
r
o
m
p
t
in
th
is
ca
s
e
is
,
“
Yo
u
ar
e
a
r
o
b
o
t
s
u
p
er
v
is
o
r
ta
s
k
ed
with
f
ix
in
g
wo
r
k
f
lo
w
er
r
o
r
s
d
u
r
in
g
d
ep
lo
y
m
e
n
t.
W
h
en
g
iv
en
a
q
u
er
y
d
escr
ib
in
g
a
n
er
r
o
r
at
a
s
p
ec
if
ic
s
tep
an
d
its
in
p
u
t
s
ch
e
m
a,
p
r
o
v
id
e
a
clea
r
,
s
p
ec
if
ic
s
o
lu
tio
n
,
n
o
t
a
g
en
er
ic
o
n
e.
T
h
e
is
s
u
e
is
n
o
t
d
u
e
to
p
o
o
r
p
r
o
g
r
am
m
in
g
b
u
t
a
m
in
o
r
d
is
cr
ep
an
cy
b
etwe
en
th
e
p
r
o
g
r
am
m
ed
lo
g
ic
an
d
g
r
o
u
n
d
tr
u
th
.
An
aly
ze
th
e
d
if
f
er
en
ce
s
b
etwe
en
th
e
d
o
cu
m
en
ts
in
th
e
wo
r
k
f
lo
w
an
d
s
u
g
g
est
h
o
w
t
o
ad
ap
t
th
e
cu
r
r
en
t
wo
r
k
f
lo
w
to
f
ix
th
e
is
s
u
e
wh
er
e
th
e
s
y
s
tem
in
co
r
r
ec
tly
id
en
tifie
s
two
p
eo
p
le
as d
if
f
er
en
t.
E
x
p
lain
w
h
y
an
d
h
o
w
th
e
er
r
o
r
o
cc
u
r
r
e
d
.
”
3.
2
.
Do
cu
m
ent
re
j
ec
t
io
n
a
na
ly
s
is
T
h
e
p
r
o
m
p
t
in
t
h
is
ca
s
e
is
,
“
As
a
s
u
p
er
v
is
o
r
,
y
o
u
r
task
is
to
ex
p
lain
d
ep
lo
y
m
en
t
er
r
o
r
s
b
ased
o
n
th
e
in
p
u
t
I
p
r
o
v
i
d
e.
Yo
u
r
r
esp
o
n
s
e
s
h
o
u
ld
b
e
s
p
ec
if
ic
t
o
th
e
er
r
o
r
d
escr
ip
tio
n
a
n
d
in
p
u
t
s
ch
e
m
a,
av
o
id
in
g
g
e
n
er
ic
f
ix
es.
T
h
e
is
s
u
e
is
lik
ely
d
u
e
t
o
a
m
is
m
atch
b
etwe
en
th
e
p
r
o
g
r
am
m
ed
lo
g
ic
an
d
th
e
ac
t
u
al
d
ata.
I
n
t
h
is
ca
s
e,
th
e
er
r
o
r
‘
Ad
d
Data
R
o
w:
Ob
j
ec
t
r
ef
er
en
ce
n
o
t
s
et
to
an
in
s
t
an
ce
o
f
an
o
b
ject
’
m
a
y
o
cc
u
r
b
ec
au
s
e
th
e
d
etails
Evaluation Warning : The document was created with Spire.PDF for Python.
I
AE
S
I
n
t
J
R
o
b
&
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u
to
m
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N:
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2
5
8
6
C
a
s
ca
d
in
g
a
u
to
m
a
ta
to
imp
r
o
ve
efficien
cy
o
f la
r
g
e
la
n
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u
a
g
e
mo
d
els a
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en
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… (
Hri
s
h
ikesh
K
.
Ha
r
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s
)
153
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T
h
ese
p
r
o
m
p
ts
wer
e
in
s
tr
u
m
en
tal
in
en
s
u
r
in
g
th
at
Gr
a
p
h
R
AG
p
r
o
v
id
ed
tar
g
eted
in
s
ig
h
ts
in
to
wo
r
k
f
lo
w
er
r
o
r
s
,
allo
win
g
u
s
to
r
ef
in
e
o
u
r
KYC
d
o
cu
m
en
t
v
alid
atio
n
an
d
r
ejec
tio
n
an
a
ly
s
is
p
r
o
ce
s
s
es.
B
y
lev
er
ag
in
g
Gr
ap
h
R
AG
’
s
r
ea
s
o
n
in
g
ca
p
ab
ilit
ies,
we
wer
e
a
b
le
to
b
r
id
g
e
d
is
cr
ep
a
n
cies
b
e
twee
n
p
r
o
g
r
am
m
e
d
lo
g
ic
an
d
g
r
o
u
n
d
t
r
u
th
,
im
p
r
o
v
in
g
th
e
o
v
er
all
ac
cu
r
ac
y
a
n
d
r
eliab
ilit
y
o
f
th
e
s
y
s
tem
.
R
eg
ex
is
ap
p
lied
in
th
e
in
itial
d
ata
v
alid
atio
n
s
tag
e,
w
h
er
e
n
am
es
an
d
ad
d
r
ess
es
in
KYC
v
er
if
icatio
n
ar
e
s
tan
d
ar
d
ized
u
s
in
g
p
r
ed
e
f
in
ed
p
atter
n
s
(
e.
g
.
,
en
s
u
r
in
g
“
J
o
h
n
S
”
a
n
d
“
J
o
h
n
Sm
ith
”
ar
e
m
atch
ed
.
Similar
ly
,
d
o
cu
m
e
n
t
r
ejec
tio
n
ca
s
es
in
th
e
in
s
u
r
an
c
e
wo
r
k
f
lo
w
u
s
e
r
eg
ex
-
b
ased
p
a
r
s
in
g
to
d
e
tect
m
is
s
in
g
f
ield
s
,
in
co
r
r
ec
t
f
o
r
m
ats,
o
r
b
l
u
r
r
y
s
ca
n
s
b
ef
o
r
e
escalatin
g
to
a
n
L
L
M
f
o
r
in
tellig
en
t
r
ea
s
o
n
in
g
.
Ad
d
itio
n
ally
,
t
h
e
wo
r
k
f
lo
w
g
r
a
p
h
tr
av
e
r
s
al
s
te
p
ca
n
in
co
r
p
o
r
ate
f
in
ite
-
s
tate
au
to
m
ata
(
DFA/NFA)
r
u
les
to
id
en
tify
k
n
o
wn
f
ailu
r
e
p
o
in
ts
d
eter
m
in
is
tically
.
B
y
in
teg
r
atin
g
r
eg
ex
-
b
ased
r
u
le
en
g
in
es
as
a
p
r
ep
r
o
ce
s
s
in
g
lay
er
b
ef
o
r
e
L
L
M
ca
lls
,
we
cr
ea
te
a
m
u
lti
-
tier
a
u
to
m
ata
ca
s
ca
d
e,
wh
er
e
d
eter
m
in
is
tic
au
to
m
ata
(
DFAs
)
h
a
n
d
le
s
im
p
le
er
r
o
r
s
,
lig
h
tweig
h
t
s
tatis
tical
m
o
d
els
d
eter
m
in
e
f
u
zz
y
m
atch
es,
an
d
L
L
Ms
s
er
v
e
as
th
e
f
in
al
f
allb
ac
k
f
o
r
a
m
b
ig
u
o
u
s
ex
ce
p
tio
n
s
.
T
h
is
wo
u
l
d
im
p
r
o
v
e
e
f
f
icien
cy
b
y
en
s
u
r
in
g
L
L
Ms
ar
e
in
v
o
k
e
d
o
n
ly
wh
en
tr
u
l
y
n
ec
ess
ar
y
,
o
p
tim
izin
g
b
o
th
en
e
r
g
y
c
o
n
s
u
m
p
tio
n
an
d
p
r
o
ce
s
s
in
g
tim
e.
T
h
e
p
r
o
m
p
t
is
f
ed
in
to
th
e
g
lo
b
al
s
ea
r
ch
s
y
s
tem
o
f
Gr
ap
h
R
AG
[
1
4
]
d
esig
n
ed
to
r
etr
iev
e
r
elev
an
t
in
f
o
r
m
atio
n
an
d
co
n
tex
t f
r
o
m
th
e
L
L
M
’
s
in
tr
in
s
ic
k
n
o
wled
g
e
o
f
th
e
d
o
m
ain
a
n
d
th
e
s
tate
o
f
th
e
p
r
o
ce
s
s
its
elf
.
T
h
e
o
u
t
p
u
t d
escr
ib
es
th
e
lo
ca
t
io
n
an
d
n
atu
r
e
o
f
th
e
er
r
o
r
an
d
o
f
f
er
s
f
ix
es.
Up
o
n
a
f
ailu
r
e
t
o
lo
ca
te/
r
eso
lv
e
t
h
e
er
r
o
r
,
t
h
e
wo
r
k
f
lo
w
s
ig
n
als th
e
n
ee
d
f
o
r
h
u
m
an
i
n
ter
v
en
tio
n
.
T
h
e
o
u
tp
u
t
o
f
Gr
ap
h
R
AG
(
i.
e.
,
er
r
o
r
r
eso
lu
tio
n
in
f
o
r
m
atio
n
)
c
o
m
b
in
e
d
with
th
e
J
SON
f
ile
o
f
th
e
wo
r
k
f
lo
w
is
f
ed
in
to
an
L
L
M
(
n
o
t
v
ia
th
e
Gr
ap
h
R
AG
f
r
am
e
wo
r
k
)
.
On
ce
t
h
e
L
L
M
h
as
p
r
o
ce
s
s
ed
th
e
in
p
u
t,
it
g
en
er
ates
th
e
co
r
r
ec
ted
s
ec
tio
n
o
f
th
e
J
SON
f
ile.
T
h
en
,
u
tili
zin
g
tr
ad
itio
n
al
s
tr
in
g
-
m
atch
i
n
g
tech
n
iq
u
es,
th
e
co
r
r
ec
ted
s
ec
tio
n
s
ar
e
p
lace
d
in
to
th
e
J
SON
f
ile.
T
h
e
L
L
M
is
ask
ed
t
o
o
u
tp
u
t
o
n
l
y
th
e
c
o
r
r
ec
ted
s
ec
tio
n
s
o
f
th
e
f
r
am
ew
o
r
k
in
th
e
i
n
ter
est
o
f
r
e
d
u
cin
g
th
e
n
u
m
b
er
o
f
to
k
en
s
g
en
er
ate
d
.
Af
te
r
th
e
co
r
r
e
ctio
n
s
ar
e
m
ad
e,
th
e
u
p
d
ated
wo
r
k
f
lo
w
is
au
to
m
atica
lly
d
ep
lo
y
ed
,
allo
win
g
th
e
p
r
o
ce
s
s
to
r
esu
m
e
with
o
u
t
t
h
e
n
ee
d
f
o
r
h
u
m
a
n
in
ter
v
en
tio
n
.
W
h
ile
th
e
af
o
r
em
en
tio
n
ed
p
ip
elin
e
co
v
er
s
th
e
1
s
t
an
d
2
n
d
c
ateg
o
r
y
o
f
o
b
s
tr
u
ctio
n
s
,
th
e
3
r
d
ca
teg
o
r
y
war
r
an
ts
a
m
o
r
e
ca
s
e
s
p
ec
if
ic
ap
p
r
o
ac
h
.
I
n
g
en
er
al,
wh
en
t
h
e
wo
r
k
f
lo
w
o
r
s
u
b
p
r
o
ce
s
s
with
in
th
e
w
o
r
k
f
l
o
w
p
r
o
v
id
es
a
n
e
g
ativ
e
o
u
t
p
u
t
(
i
n
th
e
ca
s
e
o
f
f
alse
n
eg
ativ
es
b
ein
g
p
r
o
m
in
en
t
)
,
a
g
atin
g
m
ec
h
an
is
m
(
s
u
ch
as
a
lig
h
ter
m
ac
h
in
e
lear
n
in
g
m
o
d
el
o
r
a
h
an
d
wr
itten
al
g
o
r
ith
m
)
ca
n
ca
ll
u
p
o
n
th
e
L
L
M
if
a
f
alse
n
eg
ativ
e
is
s
u
s
p
ec
ted
.
E
x
am
p
le
#
1
in
s
ec
t
io
n
4
d
etails o
u
r
ap
p
r
o
ac
h
to
t
h
is
ca
teg
o
r
y
o
f
o
b
s
tr
u
ctio
n
.
C
er
tain
ca
s
es
o
f
th
e
th
ir
d
ca
te
g
o
r
y
(
as
e
x
em
p
lifie
d
b
elo
w)
c
an
b
e
d
etec
ted
p
u
r
ely
b
y
m
ea
s
u
r
in
g
th
e
ex
ten
t
o
f
d
e
v
iatio
n
f
r
o
m
th
e
m
ea
n
o
f
v
al
u
es
p
r
o
d
u
ce
d
in
te
r
m
ed
iately
in
th
e
au
t
o
m
atio
n
p
ip
elin
e.
T
h
e
m
ea
n
an
d
s
tan
d
ar
d
d
ev
iatio
n
ca
n
e
ith
er
b
e
o
b
s
er
v
e
d
e
x
p
er
im
en
t
ally
o
r
co
n
f
i
g
u
r
e
d
m
a
n
u
ally
b
ased
o
n
d
o
m
ai
n
k
n
o
wled
g
e.
I
f
th
e
in
ter
m
ed
iate
o
u
tp
u
ts
ar
e
s
tr
in
g
s
,
we
m
ay
u
s
e
n
o
n
-
d
a
ta
d
r
iv
en
em
b
e
d
d
in
g
tech
n
iq
u
es
s
u
ch
as
TF
-
I
DF
v
ec
to
r
izatio
n
.
On
ce
t
h
e
T
F
-
I
DF
v
ec
to
r
s
h
a
v
e
b
ee
n
cr
ea
ted
,
C
o
s
in
e
Similar
ity
is
u
s
ed
to
c
o
m
p
ar
e
th
e
v
ec
to
r
s
f
o
r
d
if
f
e
r
en
t
f
ield
s
.
C
o
s
in
e
Similar
ity
ca
lcu
lates
th
e
an
g
le
b
etwe
en
two
v
ec
to
r
s
an
d
ass
ig
n
s
a
s
co
r
e
r
an
g
in
g
f
r
o
m
0
to
1
,
with
1
in
d
icatin
g
p
er
f
ec
t
s
im
ilar
ity
an
d
0
in
d
icatin
g
n
o
s
im
ilar
ity
.
T
h
is
s
co
r
e
q
u
an
tifie
s
th
e
s
im
ilar
ity
o
f
two
f
ield
s
,
e
v
en
if
th
ey
ar
e
n
o
t
id
en
tical,
b
y
f
o
cu
s
in
g
o
n
th
e
r
elatio
n
s
h
i
p
b
etwe
en
th
e
ter
m
s
in
ea
ch
.
T
h
e
th
r
esh
o
ld
is
esta
b
lis
h
ed
th
r
o
u
g
h
ex
p
e
r
im
en
tati
o
n
an
d
an
al
y
s
is
o
f
v
ar
i
o
u
s
d
o
cu
m
en
ts
;
we
f
in
e
-
tu
n
e
an
d
s
et
th
e
th
r
esh
o
ld
to
alig
n
with
th
e
wo
r
k
f
lo
w
’
s
p
r
ac
tical
n
ee
d
s
.
T
h
is
ap
p
r
o
ac
h
en
ab
les
th
e
s
y
s
tem
to
ef
f
icien
tly
au
to
m
ate
d
o
c
u
m
e
n
t
v
er
if
icatio
n
,
f
lag
g
in
g
o
n
ly
ca
s
es
th
at
f
all
b
elo
w
th
e
t
h
r
esh
o
ld
f
o
r
f
u
r
th
er
an
aly
s
is
,
im
p
r
o
v
in
g
th
e
p
r
o
ce
s
s
’
s
s
p
ee
d
an
d
s
ca
lab
ilit
y
.
3
.
3
.
M
a
t
hema
t
ica
l
f
o
rm
ula
t
i
o
n
T
o
f
o
r
m
alize
our
p
ip
elin
e,
c
o
n
s
id
er
th
e
R
PA w
o
r
k
f
lo
w
as a
d
ir
ec
ted
g
r
ap
h
=
(
,
)
,
wh
er
e:
=
{
1
,
2
,
…
,
}
ar
e
th
e
lo
g
ical
s
tep
s
(
d
ec
is
io
n
b
lo
ck
s
,
ac
tio
n
s
)
⊆
×
ar
e
th
e
ex
ec
u
tio
n
ed
g
es c
ap
tu
r
in
g
f
lo
w.
W
h
en
an
ex
ce
p
tio
n
o
cc
u
r
s
at
node
v
k
,
we
b
u
ild
a
s
u
b
g
r
ap
h
co
n
tex
t
=
(
,
)
,
=
{
:
(
,
)
≤
ℓ
}
,
wh
er
e
(
⋅
,
⋅
)
is
g
r
ap
h
d
is
tan
ce
an
d
ℓ
a
co
n
tex
t w
in
d
o
w
s
ize.
Def
in
e
a
p
r
o
m
p
t c
o
n
s
tr
u
ctio
n
f
u
n
ctio
n
=
(
,
,
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
2
5
8
6
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
,
Vo
l
.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
1
49
-
1
61
154
wh
er
e
ϕ
co
n
ca
ten
ates th
e
er
r
o
r
m
ess
ag
e,
n
o
d
e
m
etad
ata
an
d
th
e
s
er
ialized
g
r
ap
h
co
n
te
x
t.
T
h
e
Gr
ap
h
R
AG
r
etr
iev
al
m
o
d
u
le
s
elec
ts
to
p
-
m
r
elev
an
t
n
o
d
es b
y
a
s
co
r
in
g
f
u
n
cti
o
n
:
(
,
)
=
(
,
)
+
⋅
(
)
wh
er
e
is
th
e
em
b
ed
d
in
g
o
f
n
o
d
e
v
i,
s
im
(
⋅
,
⋅
)
is
co
s
in
e
s
im
i
lar
ity
in
em
b
ed
d
in
g
s
p
ac
e,
d
eg
(
)
is
th
e
n
o
d
e
-
d
eg
r
ee
b
ias,
λ
b
alan
ce
s
co
n
tex
t v
s
.
s
tr
u
ctu
r
al
im
p
o
r
tan
ce
.
T
h
e
L
L
M
r
esp
o
n
s
e
R
=L
L
M(
P)
o
u
tp
u
ts
a
co
r
r
ec
ted
s
u
b
g
r
a
p
h
=
(
,
)
.
W
e
in
teg
r
ate
co
r
r
ec
tio
n
s
v
ia
s
tr
in
g
p
atch
in
g
in
to
th
e
o
r
ig
in
al
J
SON:
′
=
⊕
.
3
.
4
.
P
s
eudo
co
de
Hav
in
g
estab
lis
h
ed
m
ath
em
atica
l
f
o
r
m
u
latio
n
,
th
e
p
i
p
elin
e
ca
n
b
e
tr
an
s
lated
in
to
a
p
r
o
c
ed
u
r
e
th
at
r
ef
lects
h
o
w
th
e
s
y
s
tem
b
eh
av
es
d
u
r
in
g
r
u
n
tim
e.
Alg
o
r
ith
m
1
d
ef
in
es
th
e
f
u
ll
ex
ec
u
tio
n
lo
o
p
o
f
th
e
R
PA
b
o
t.
s
h
o
win
g
h
o
w
th
e
wo
r
k
f
lo
w
a
d
v
an
ce
s
,
d
etec
ts
ex
ce
p
tio
n
s
,
co
n
s
tr
u
cts
co
n
tex
t,
r
etr
iev
es
r
elev
an
t
in
f
o
r
m
atio
n
th
r
o
u
g
h
Gr
a
p
h
R
AG,
an
d
ap
p
lies
L
L
M
g
en
er
ate
d
co
r
r
ec
t
io
n
s
b
ef
o
r
e
co
n
tin
u
in
g
ex
ec
u
tio
n
.
Alg
o
r
ith
m
2
o
u
tlin
es
th
e
r
etr
iev
al
an
d
d
e
cisi
o
n
lo
g
ic
u
s
ed
b
y
th
e
ag
e
n
tic
L
L
M
s
u
p
er
v
is
o
r
to
s
co
r
e
wo
r
k
f
lo
w
n
o
d
es,
ass
em
b
le
co
n
tex
tu
al
ev
id
e
n
ce
,
an
d
g
e
n
er
ate
an
ac
ti
o
n
p
la
n
th
r
o
u
g
h
th
e
L
L
M.
Alg
o
r
ith
m
1
.
C
ascad
in
g
Au
to
m
ataWit
h
Gr
ap
h
R
AG(
G,
th
r
esh
o
ld
τ
)
Input: RPA graph G, gating threshold τ
Output: Updated graph G
’
for each execution step v_k in G do
try
execute(v_k)
catch error e at node v_k:
// Preprocess graph context
G_k ← extractSubgraph(G, v_k, window=ℓ)
// Build prompt
P ← buildPrompt(e, v_k, G_k)
// Retrieve relevant graph context
C ← GraphRAG.retrieve(G_k, P, top_m)
// Query LLM for correction
ΔG_k ← LLM.resolveExceptions(P, C)
// Patch workflow
G ← applyPatch(G, ΔG_k)
resume execution at v_k
end for
return G
Alg
o
r
ith
m
2
.
Ag
en
ticL
L
MSu
p
er
v
is
o
r
(
P,
em
b
e
d
d
in
g
s
)
Input: prompt P, node embeddings embeddings
Output: action plan A
// Structured retrieval
scores ← [sim(e_i, P) + λ deg(i) for i in nodes]
C ← selectTop(scores, m)
// LLM planning
A ← LLM.generatePlan(P, context=C)
return A
4.
I
M
P
L
E
M
E
NT
A
T
I
O
N
A
ND
RE
SU
L
T
S
T
h
e
p
r
o
p
o
s
ed
p
i
p
elin
e
was
im
p
lem
en
ted
o
n
two
r
ea
l
w
o
r
ld
s
ce
n
a
r
io
s
f
r
o
m
t
h
e
b
an
k
in
g
a
n
d
in
s
u
r
an
ce
s
ec
to
r
s
wh
er
e
R
P
A
is
s
ee
in
g
m
ass
iv
e
ad
o
p
tio
n
[
3
]
.
Ou
r
wo
r
k
is
av
ailab
le
o
n
GitHu
b
f
o
r
f
u
ll
r
ep
r
o
d
u
cib
ilit
y
.
Fo
r
i
d
en
tifie
d
s
ce
n
ar
io
s
,
au
to
m
ata
wer
e
d
ev
elo
p
e
d
u
s
in
g
UiPath
.
Mic
r
o
s
o
f
t
’
s
o
r
ig
in
al
im
p
lem
en
tatio
n
o
f
Gr
ap
h
R
AG
with
GPT
-
4
o
th
r
o
u
g
h
Op
en
A
PI
was u
s
ed
.
4
.
1
.
E
x
a
m
ple 1
:
enha
ncing
do
cum
ent
v
a
lid
a
t
io
n
in K
Y
C
I
n
in
d
u
s
tr
ies lik
e
b
an
k
in
g
an
d
f
in
an
ce
,
au
to
m
atin
g
Kn
o
w
Yo
u
r
C
u
s
to
m
er
(
KYC)
p
r
o
ce
s
s
es
is
cr
itica
l
f
o
r
co
m
p
lian
ce
a
n
d
f
r
au
d
p
r
ev
en
tio
n
.
Ho
wev
er
,
d
is
cr
ep
a
n
cies
in
cu
s
to
m
er
in
f
o
r
m
atio
n
ac
r
o
s
s
d
o
cu
m
en
ts
ca
u
s
e
ex
ce
p
tio
n
s
an
d
p
o
s
e
ch
allen
g
es
f
o
r
tr
ad
itio
n
al
R
PA
s
y
s
tem
s
.
E
v
en
m
in
o
r
v
ar
iatio
n
s
lik
e
ex
p
an
s
io
n
o
r
co
n
tr
ac
tio
n
o
f
s
u
r
n
am
es
o
r
m
id
d
le
n
am
es
—
in
o
u
r
in
p
u
ts
wh
ile
im
p
lem
en
tin
g
a
KYC
v
er
if
icatio
n
p
r
o
ce
s
s
s
u
ch
as
“
J
o
h
n
S
”
o
n
o
n
e
d
o
cu
m
en
t
an
d
“
J
o
h
n
Sm
ith
”
o
n
an
o
th
er
,
o
r
d
if
f
e
r
en
ce
s
in
ad
d
r
es
s
d
etails
lik
e
“
123
E
lm
Stre
et
”
v
er
s
u
s
“
1
2
3
E
lm
(
f
o
r
m
e
r
ly
B
ak
er
Stre
et)
Stre
et,
Op
p
.
C
af
f
e
Ho
u
s
e
”
—
ca
u
s
e
d
th
e
au
to
m
atio
n
to
Evaluation Warning : The document was created with Spire.PDF for Python.
I
AE
S
I
n
t
J
R
o
b
&
A
u
to
m
I
SS
N:
2722
-
2
5
8
6
C
a
s
ca
d
in
g
a
u
to
m
a
ta
to
imp
r
o
ve
efficien
cy
o
f la
r
g
e
la
n
g
u
a
g
e
mo
d
els a
g
en
ts
… (
Hri
s
h
ikesh
K
.
Ha
r
ita
s
)
155
m
is
in
ter
p
r
et
th
em
as
s
ep
ar
ate
in
d
iv
id
u
als.
T
h
is
r
esu
lts
in
m
an
u
al
in
ter
v
en
tio
n
,
d
im
i
n
is
h
in
g
th
e
ef
f
icien
cy
o
f
th
e
au
to
m
atio
n
.
T
o
o
v
er
c
o
m
e
th
is
,
in
teg
r
atin
g
lar
g
e
lan
g
u
ag
e
m
o
d
els
(
L
L
M
s
)
in
to
R
PA
wo
r
k
f
lo
ws
as
im
p
lem
en
ted
ca
n
d
r
am
atica
lly
en
h
an
ce
d
o
c
u
m
en
t
v
alid
atio
n
.
L
L
Ms,
with
th
eir
s
o
p
h
is
ticated
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
,
ca
n
in
tellig
en
tly
in
ter
p
r
et
s
u
c
h
d
is
cr
ep
a
n
cies
an
d
ass
ess
wh
eth
er
th
e
d
o
cu
m
en
ts
r
ef
er
to
t
h
e
s
am
e
p
er
s
o
n
.
Fo
r
in
s
tan
ce
,
th
ey
d
eter
m
i
n
e
th
at
“
J
o
h
n
S
”
a
n
d
“
J
o
h
n
Sm
ith
”
li
k
ely
b
el
o
n
g
to
th
e
s
am
e
in
d
i
v
id
u
al
b
y
a
n
aly
zin
g
co
n
tex
tu
al
cu
es,
s
u
c
h
as a
d
d
r
e
s
s
s
im
ilar
it
ies o
r
o
th
er
co
r
r
o
b
o
r
atin
g
in
f
o
r
m
atio
n
.
I
n
co
r
p
o
r
atin
g
L
L
Ms
elim
in
at
es
th
e
n
ee
d
f
o
r
m
an
u
al
r
eso
l
u
tio
n
b
y
en
ab
lin
g
a
u
to
m
ated
s
y
s
tem
s
to
ac
co
u
n
t
f
o
r
t
h
ese
s
u
b
tle
v
ar
i
atio
n
s
,
s
tr
ea
m
lin
in
g
th
e
KYC
p
r
o
ce
s
s
.
T
h
is
r
esu
lts
in
g
r
ea
ter
ac
cu
r
ac
y
,
f
aster
p
r
o
ce
s
s
in
g
,
an
d
r
e
d
u
ce
d
o
p
er
atio
n
al
b
o
ttlen
ec
k
s
.
R
esear
ch
h
ig
h
lig
h
ts
h
o
w
d
is
cr
ep
an
cie
s
b
etwe
en
o
f
f
icial
d
o
cu
m
e
n
ts
f
r
eq
u
en
tly
lead
t
o
f
alse
r
ejec
tio
n
s
b
y
R
PA
s
y
s
tem
s
,
u
n
d
er
s
co
r
in
g
t
h
e
n
e
ed
f
o
r
AI
-
e
n
h
an
ce
d
s
o
lu
tio
n
s
to
m
in
im
ize
s
u
ch
e
r
r
o
r
s
an
d
en
s
u
r
e
co
m
p
lian
ce
.
I
n
th
e
d
o
cu
m
e
n
t
v
e
r
if
icatio
n
wo
r
k
f
lo
w
illu
s
tr
ated
in
Fig
u
r
e
3
,
it
is
cr
u
cial
to
d
eter
m
i
n
e
wh
eth
er
to
in
v
o
k
e
t
h
e
L
L
M,
esp
ec
ially
in
s
ce
n
ar
io
s
wh
er
e
n
o
er
r
o
r
s
ar
e
p
r
esen
t
a
n
d
th
e
L
L
M
i
s
b
ein
g
u
s
ed
j
u
s
t
s
u
p
er
v
is
o
r
.
Fre
q
u
e
n
t
ca
lls
t
o
LLM
f
o
r
o
v
er
s
ig
h
t
ca
n
r
esu
lt
in
s
ig
n
if
ica
n
t
co
m
p
u
t
atio
n
al
o
v
er
h
ea
d
,
u
n
d
er
m
i
n
in
g
th
e
ef
f
icien
cy
o
f
th
e
au
t
o
m
atio
n
p
r
o
ce
s
s
.
T
o
ad
d
r
ess
th
is
ch
allen
g
e,
a
s
im
ilar
ity
s
co
r
e
is
ca
lcu
lated
.
I
n
o
u
r
ex
am
p
le,
w
e
u
s
e
a
T
F
-
I
DF
v
ec
to
r
izer
to
e
m
b
ed
th
e
ca
n
d
id
ate
d
o
c
u
m
en
t
s
,
co
s
in
e
s
im
ilar
ity
b
etwe
en
th
e
v
ec
to
r
s
p
r
o
d
u
ce
s
a
s
co
r
e.
C
ases
wh
er
e
th
e
s
im
ilar
ity
s
co
r
e
is
ab
o
v
e
a
t
h
r
esh
o
ld
(
d
eter
m
i
n
ed
ex
p
er
im
en
tally
)
in
d
icate
a
v
er
if
icatio
n
m
is
m
atch
r
ath
e
r
t
h
an
a
s
u
b
s
tan
tiv
e
d
is
cr
ep
an
c
y
,
th
e
L
L
M
is
n
o
t
in
v
o
k
e
d
,
th
er
e
b
y
o
p
tim
izin
g
r
eso
u
r
ce
u
tili
za
tio
n
wh
ile
m
ain
tain
in
g
th
e
in
teg
r
ity
o
f
th
e
wo
r
k
f
lo
w.
Fig
u
r
e
3
.
E
x
am
p
le
wo
r
k
f
lo
w
f
o
r
ef
f
icien
t L
L
M
s
u
p
er
v
is
io
n
t
o
co
m
b
at
u
n
p
r
ed
ictab
ly
v
ar
y
in
g
in
p
u
t
4
.
2
.
E
x
a
m
ple 2
:
do
cu
m
ent
r
ej
ec
t
io
n a
na
ly
s
is
I
n
au
t
o
m
ated
p
r
o
ce
s
s
es
th
at
r
e
q
u
ir
e
d
o
cu
m
e
n
t
u
p
lo
ad
s
—
s
u
c
h
as
in
s
u
r
a
n
ce
claim
s
,
l
o
an
a
p
p
licatio
n
s
,
o
r
r
eg
u
lato
r
y
f
ilin
g
s
—
d
o
cu
m
en
t
r
ejec
tio
n
is
a
co
m
m
o
n
h
u
r
d
le.
T
h
ese
r
ejec
tio
n
s
ca
n
o
cc
u
r
f
o
r
v
a
r
io
u
s
r
ea
s
o
n
s
,
in
clu
d
in
g
m
is
s
in
g
i
n
f
o
r
m
atio
n
,
in
co
r
r
ec
t
f
o
r
m
ats,
o
r
p
o
o
r
d
o
cu
m
en
t
q
u
ality
.
T
r
ad
itio
n
al
R
PA
s
y
s
tem
s
o
f
ten
r
ejec
t
d
o
cu
m
e
n
ts
with
o
u
t
p
r
o
v
i
d
in
g
d
etail
ed
r
ea
s
o
n
s
,
leav
in
g
u
s
er
s
o
r
ad
m
in
is
tr
ato
r
s
to
in
v
esti
g
ate
th
e
is
s
u
e
m
an
u
ally
,
wh
ich
d
im
in
is
h
es th
e
ef
f
ec
tiv
en
ess
o
f
au
to
m
atio
n
.
I
n
o
u
r
s
tu
d
y
,
we
g
en
er
ated
t
wo
k
n
o
wled
g
e
g
r
a
p
h
s
to
en
h
an
ce
d
o
cu
m
en
t
v
alid
atio
n
a
n
d
r
ejec
tio
n
an
aly
s
is
with
in
th
e
KYC
wo
r
k
f
lo
w.
T
h
ese
k
n
o
wled
g
e
g
r
ap
h
s
,
s
h
o
wn
in
Fig
u
r
es
4
a
n
d
5
,
wer
e
co
n
s
tr
u
cted
to
ca
p
tu
r
e
r
elatio
n
s
h
ip
s
b
etwe
en
d
o
c
u
m
en
t
attr
i
b
u
tes,
wo
r
k
f
lo
w
er
r
o
r
s
,
an
d
co
r
r
ec
tiv
e
ac
tio
n
s
,
en
ab
lin
g
m
o
r
e
p
r
ec
is
e
is
s
u
e
r
eso
lu
tio
n
.
B
y
lev
er
ag
in
g
t
h
ese
s
tr
u
ctu
r
ed
r
ep
r
esen
tatio
n
s
,
we
im
p
r
o
v
ed
th
e
s
y
s
tem
’
s
ab
ilit
y
to
d
etec
t a
n
d
ad
d
r
ess
d
is
cr
ep
an
ci
es b
etwe
en
p
r
o
g
r
am
m
ed
lo
g
ic
an
d
r
ea
l
-
wo
r
ld
d
ata.
An
id
ea
l
s
o
lu
tio
n
is
to
in
te
g
r
ate
L
L
Ms
in
to
th
e
r
ejec
tio
n
a
n
aly
s
is
p
r
o
ce
s
s
,
en
ab
lin
g
t
h
e
s
y
s
tem
to
p
r
o
v
id
e
a
n
in
tellig
en
t,
clea
r
ex
p
lan
atio
n
f
o
r
wh
y
a
d
o
c
u
m
e
n
t
was
r
ejec
ted
.
Fo
r
in
s
tan
ce
,
in
th
e
ca
s
e
o
f
an
in
s
u
r
an
ce
claim
,
th
e
in
p
u
t
is
a
b
lu
r
r
y
p
h
o
to
o
f
a
r
eq
u
ir
ed
m
ed
ical
b
ill,
th
e
L
L
M
ex
am
in
es
th
e
d
o
cu
m
en
t
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
7
2
2
-
2
5
8
6
I
AE
S
I
n
t
J
R
o
b
&
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u
to
m
,
Vo
l
.
1
5
,
No
.
1
,
Ma
r
ch
20
2
6
:
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49
-
1
61
156
id
en
tifie
s
th
at
th
e
r
ejec
tio
n
is
d
u
e
to
its
u
n
r
ea
d
ab
le
o
r
b
l
u
r
r
y
n
atu
r
e.
T
h
e
L
L
M
th
en
g
e
n
er
a
tes
an
ex
p
lan
atio
n
,
“
Do
cu
m
en
t
r
ejec
ted
d
u
e
to
b
lu
r
r
in
ess
,
r
en
d
er
i
n
g
k
ey
in
f
o
r
m
atio
n
(
e.
g
.
,
in
v
o
ice
n
u
m
b
er
,
to
tal
am
o
u
n
t)
illeg
ib
le.
”
T
h
is
ca
p
ab
ilit
y
allo
ws
th
e
L
L
M
to
n
o
t
o
n
ly
f
lag
th
e
is
s
u
e
b
u
t
also
o
f
f
er
s
p
ec
if
ic
g
u
id
an
ce
f
o
r
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ad
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ates
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k
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o
r
r
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aso
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im
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ich
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ig
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ican
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d
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k
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h
e
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teg
r
atio
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o
f
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L
Ms
ca
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ap
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d
iag
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u
r
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u
r
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ased
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158
5.
CO
NCLU
SI
O
N
Ou
r
p
ip
elin
e
o
f
e
x
p
ed
ie
n
tly
i
n
v
o
k
in
g
L
L
Ms
f
o
r
e
x
ce
p
tio
n
h
an
d
lin
g
an
d
s
u
p
er
v
is
io
n
d
e
m
o
n
s
tr
ates
s
ig
n
if
ican
t
p
o
ten
tial
in
ad
v
a
n
cin
g
t
r
u
e
h
y
p
er
-
au
t
o
m
atio
n
.
B
y
s
tr
ateg
ically
i
n
teg
r
atin
g
L
L
Ms
o
n
ly
w
h
e
n
n
ec
ess
ar
y
,
o
u
r
a
p
p
r
o
ac
h
ef
f
ec
t
iv
ely
ad
d
r
ess
es
o
n
e
o
f
th
e
m
o
s
t
p
er
s
is
ten
t
ch
allen
g
es
in
R
PA
—
th
e
h
an
d
lin
g
o
f
ex
ce
p
tio
n
s
—
with
o
u
t
th
e
co
n
s
tan
t
r
elian
ce
o
n
co
m
p
u
tatio
n
ally
ex
p
en
s
iv
e
a
n
d
h
ig
h
-
lat
en
cy
m
o
d
els.
T
h
is
s
elec
tiv
e
in
v
o
ca
tio
n
n
o
t
o
n
ly
o
p
tim
izes
r
eso
u
r
ce
u
tili
za
tio
n
b
u
t
also
e
n
s
u
r
es
th
at
au
to
m
atio
n
wo
r
k
f
lo
ws
r
em
ain
ef
f
icien
t,
r
esp
o
n
s
iv
e,
a
n
d
s
ca
lab
le.
T
h
r
o
u
g
h
o
u
r
two
ca
s
e
s
tu
d
ie
s
,
we
illu
s
tr
ate
h
o
w
th
is
wo
r
k
f
lo
w
h
as
th
e
p
o
ten
tial
to
r
e
d
ef
in
e
th
e
au
to
m
atio
n
lan
d
s
ca
p
e
b
y
e
n
ab
lin
g
m
o
r
e
in
tellig
en
t,
co
n
tex
t
-
awa
r
e
d
ec
is
io
n
-
m
ak
in
g
with
in
R
PA
s
y
s
tem
s
.
T
h
e
ab
ilit
y
to
s
ea
m
less
ly
in
teg
r
ate
r
u
le
-
b
ased
m
ec
h
an
is
m
s
with
L
L
M
-
p
o
wer
ed
ex
ce
p
tio
n
h
a
n
d
lin
g
co
n
tr
ib
u
tes
to
g
r
ea
ter
s
y
s
tem
ad
ap
tab
ilit
y
a
n
d
r
eliab
ilit
y
,
r
ed
u
cin
g
m
a
n
u
al
in
ter
v
en
tio
n
wh
ile
m
ain
tai
n
in
g
ac
cu
r
ac
y
.
T
h
is
ad
v
an
ce
m
e
n
t
co
u
ld
s
ig
n
if
ica
n
tly
en
h
an
ce
th
e
a
d
o
p
tio
n
an
d
u
b
iq
u
ity
o
f
R
PA
ac
r
o
s
s
v
ar
io
u
s
in
d
u
s
tr
ies,
f
u
r
th
er
b
r
id
g
in
g
th
e
g
a
p
b
etwe
en
tr
a
d
itio
n
al
au
to
m
atio
n
an
d
AI
-
d
r
iv
en
co
g
n
itiv
e
au
to
m
ati
o
n
.
C
o
n
s
eq
u
en
tly
,
o
u
r
ap
p
r
o
ac
h
s
er
v
es
as
a
f
o
u
n
d
atio
n
al
s
tep
to
war
d
r
ea
lizin
g
f
u
ll
y
au
to
n
o
m
o
u
s
an
d
r
esil
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t
au
to
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atio
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p
i
p
elin
es
in
en
ter
p
r
is
e
en
v
ir
o
n
m
en
ts
.
6.
F
UT
UR
E
WO
RK
T
h
er
e
ar
e
s
ev
er
al
wo
r
k
s
to
b
e
ex
p
ec
ted
in
th
e
f
u
tu
r
e.
T
h
e
f
ir
s
t
is
p
r
ev
en
tin
g
L
L
M
h
allu
cin
atio
n
.
L
L
Ms
f
r
eq
u
en
tly
g
e
n
er
ate
in
a
cc
u
r
ate
in
f
o
r
m
atio
n
o
r
i
n
tr
o
d
u
ce
s
ec
u
r
ity
v
u
ln
e
r
ab
ilit
ies
w
ith
in
R
PA
s
y
s
tem
s
.
Ad
d
r
ess
in
g
th
is
ch
allen
g
e
is
a
n
o
n
g
o
in
g
r
esear
ch
ar
ea
w
h
er
e
m
eth
o
d
o
lo
g
ies
s
u
ch
as
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
with
h
u
m
a
n
f
ee
d
b
ac
k
(
R
L
H
F),
r
etr
iev
al
-
a
u
g
m
e
n
ted
g
en
e
r
atio
n
(
R
AG)
,
an
d
s
tr
u
ctu
r
e
d
p
r
o
m
p
tin
g
h
av
e
d
em
o
n
s
tr
ated
p
o
ten
tial
in
m
itig
atin
g
h
allu
cin
atio
n
.
Ad
d
itio
n
ally
,
ad
v
a
n
ce
m
en
ts
in
ad
v
er
s
ar
ial
test
in
g
an
d
f
in
e
-
tu
n
e
d
d
o
m
ain
-
s
p
ec
if
ic
m
o
d
els f
u
r
th
e
r
co
n
tr
i
b
u
te
to
im
p
r
o
v
in
g
r
eliab
ilit
y
.
S
e
c
o
n
d
,
i
t
i
s
u
n
i
f
i
e
d
t
e
c
h
n
i
q
u
es
f
o
r
h
e
u
r
i
s
t
i
c
d
e
s
i
g
n
.
T
h
e
d
e
v
e
l
o
p
m
e
n
t
o
f
l
i
g
h
t
w
e
i
g
h
t
h
e
u
r
i
s
t
i
c
m
o
d
e
ls
a
n
d
r
u
l
e
-
b
a
s
e
d
s
y
s
te
m
s
t
o
d
e
te
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m
i
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e
t
h
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c
e
s
s
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t
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f
L
L
M
i
n
v
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t
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s
c
r
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c
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f
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t
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c
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p
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t
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l
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f
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c
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n
c
y
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n
d
m
i
n
i
m
i
z
i
n
g
c
o
s
ts
.
T
e
c
h
n
i
q
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e
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s
u
c
h
a
s
f
e
at
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r
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x
t
r
a
c
t
i
o
n
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s
t
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is
t
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ca
l
a
n
o
m
a
l
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t
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c
t
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n
d
e
d
g
e
-
b
a
s
e
d
A
I
i
m
p
l
e
m
e
n
t
a
ti
o
n
s
en
a
b
l
e
p
r
e
c
is
e
h
e
u
r
is
ti
c
d
es
i
g
n
t
h
a
t
e
n
s
u
r
es
L
L
Ms
a
r
e
lev
e
r
a
g
e
d
o
n
l
y
w
h
e
n
n
e
c
e
s
s
a
r
y
,
t
h
e
r
e
b
y
i
m
p
r
o
v
i
n
g
r
e
s
p
o
n
s
e
ti
m
e
s
a
n
d
r
e
d
u
c
i
n
g
u
n
n
e
c
e
s
s
a
r
y
c
o
m
p
u
t
a
t
i
o
n
a
l
o
v
e
r
h
e
a
d
.
T
h
e
th
ir
d
o
n
e
is
ex
h
au
s
tiv
e
t
esti
n
g
ac
r
o
s
s
d
o
m
ain
s
an
d
u
s
e
ca
s
es
.
W
h
ile
th
is
s
tu
d
y
illu
s
tr
ates
th
e
ef
f
icac
y
o
f
o
u
r
p
i
p
elin
e
in
t
h
e
b
an
k
in
g
an
d
in
s
u
r
a
n
ce
in
d
u
s
tr
ies,
co
m
p
r
eh
e
n
s
iv
e
v
alid
a
tio
n
ac
r
o
s
s
d
iv
er
s
e
s
ec
to
r
s
—
s
u
ch
as
h
ea
lth
ca
r
e,
s
u
p
p
ly
ch
ain
m
an
a
g
em
en
t,
an
d
leg
al
a
u
to
m
atio
n
—
wo
u
l
d
p
r
o
v
id
e
a
m
o
r
e
r
o
b
u
s
t
ev
alu
atio
n
.
E
s
tab
lis
h
in
g
b
en
ch
m
ar
k
d
atasets
,
r
ea
l
-
wo
r
ld
s
tr
ess
te
s
tin
g
,
an
d
d
e
f
in
in
g
f
o
r
m
al
q
u
an
titativ
e
ass
es
s
m
en
t
f
r
am
ewo
r
k
s
in
c
o
r
p
o
r
atin
g
f
ac
to
r
s
lik
e
ac
cu
r
a
cy
,
laten
cy
,
an
d
r
o
b
u
s
tn
ess
u
n
d
e
r
ad
v
er
s
ar
ial
co
n
d
itio
n
s
ar
e
ess
en
tial to
en
h
an
ce
th
e
g
e
n
er
aliza
b
ilit
y
o
f
t
h
e
ap
p
r
o
ac
h
.
T
h
e
n
e
x
t
is
m
u
l
ti
-
t
i
e
r
c
a
s
c
a
d
e
s
f
o
r
s
c
a
l
a
b
il
i
t
y
.
T
h
e
p
r
o
p
o
s
e
d
a
r
c
h
i
t
e
c
t
u
r
e
c
u
r
r
e
n
t
l
y
i
n
te
g
r
a
t
es
tw
o
l
e
v
e
ls
o
f
c
a
s
c
a
d
i
n
g
—
r
u
l
e
-
b
as
e
d
a
u
t
o
m
a
t
a
a
n
d
L
L
M
a
g
e
n
t
s
—
b
u
t
f
o
r
l
a
r
g
e
-
s
c
a
l
e
d
e
p
l
o
y
m
e
n
t
s
i
n
v
o
l
v
i
n
g
m
i
l
l
i
o
n
s
o
f
t
r
a
n
s
a
c
ti
o
n
s
,
a
d
d
i
t
i
o
n
a
l
l
a
y
e
r
s
o
f
p
r
o
c
e
s
s
i
n
g
c
o
u
l
d
b
e
i
n
t
r
o
d
u
c
e
d
.
T
h
e
s
e
m
a
y
i
n
c
l
u
d
e
s
p
e
c
i
al
i
z
e
d
d
o
m
ai
n
-
s
p
e
c
i
f
i
c
L
L
Ms
,
h
y
b
r
i
d
a
r
c
h
it
e
ct
u
r
e
s
i
n
c
o
r
p
o
r
a
ti
n
g
F
u
z
z
y
A
u
to
m
a
t
a
,
a
n
d
r
e
i
n
f
o
r
c
e
m
e
n
t
l
e
a
r
n
i
n
g
-
b
a
s
e
d
d
e
c
is
i
o
n
s
y
s
te
m
s
t
h
at
d
y
n
a
m
i
c
a
ll
y
a
d
j
u
s
t
p
r
o
c
e
s
s
i
n
g
f
l
o
w
s
b
as
e
d
o
n
c
o
n
t
e
x
t
a
n
d
h
i
s
t
o
r
ic
a
l
p
e
r
f
o
r
m
a
n
ce
m
e
t
r
i
cs
.
W
e
also
n
ee
d
ad
ap
tiv
e
m
o
d
el
s
elec
tio
n
an
d
co
n
tin
u
o
u
s
lear
n
in
g
.
T
o
en
h
an
ce
ad
a
p
tab
ilit
y
,
d
y
n
am
ic
m
o
d
el
s
elec
tio
n
m
ec
h
an
is
m
s
ca
n
b
e
in
teg
r
ate
d
,
allo
win
g
t
h
e
s
y
s
tem
to
s
witch
b
etwe
en
d
if
f
er
en
t
L
L
Ms
o
r
h
eu
r
is
tic
m
eth
o
d
s
b
ased
o
n
co
n
tex
tu
al
r
eq
u
ir
em
en
ts
.
Ad
d
itio
n
ally
,
in
co
r
p
o
r
ati
n
g
co
n
tin
u
o
u
s
lear
n
in
g
m
ec
h
an
is
m
s
—
s
u
ch
as
f
ed
er
at
ed
lear
n
in
g
an
d
s
elf
-
s
u
p
er
v
is
e
d
tr
ain
in
g
—
ca
n
h
elp
im
p
r
o
v
e
m
o
d
el
p
er
f
o
r
m
an
ce
o
v
er
tim
e
b
y
ass
im
ilatin
g
r
ea
l
-
wo
r
ld
f
ee
d
b
ac
k
a
n
d
d
o
m
ain
-
s
p
ec
if
ic
u
p
d
ates.
T
h
e
s
ec
o
n
d
last
is
r
o
b
u
s
t
ex
p
lain
ab
ilit
y
an
d
in
ter
p
r
eta
b
ilit
y
f
r
am
ewo
r
k
s
,
e
n
s
u
r
i
n
g
th
at
th
e
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es
o
f
L
L
Ms
r
em
ain
tr
an
s
p
ar
e
n
t
is
a
f
u
n
d
am
en
tal
r
e
q
u
ir
em
en
t
f
o
r
en
ter
p
r
is
e
ad
o
p
tio
n
,
p
ar
ticu
lar
ly
in
r
e
g
u
lated
in
d
u
s
tr
ies.
I
n
co
r
p
o
r
atin
g
e
x
p
l
ain
ab
ilit
y
tech
n
iq
u
es
s
u
ch
as
atten
tio
n
-
b
ased
v
is
u
aliza
tio
n
,
ca
u
s
al
in
f
er
en
c
e
m
eth
o
d
s
,
an
d
p
o
s
t
-
h
o
c
in
ter
p
r
etab
ilit
y
m
o
d
els
wo
u
ld
en
h
an
ce
u
s
er
tr
u
s
t
an
d
r
eg
u
lato
r
y
co
m
p
lian
ce
.
Fin
ally
,
it
is
s
ec
u
r
ity
an
d
ad
v
er
s
ar
ial
r
o
b
u
s
tn
ess
m
ea
s
u
r
es
.
A
s
L
L
Ms
ar
e
in
cr
ea
s
in
g
ly
in
teg
r
ated
in
to
cr
itical
au
to
m
atio
n
wo
r
k
f
lo
ws,
en
s
u
r
in
g
th
eir
r
esil
ien
ce
a
g
ain
s
t
ad
v
er
s
ar
ial
attac
k
s
is
p
ar
a
m
o
u
n
t.
T
ec
h
n
i
q
u
es
s
u
ch
as
ad
v
e
r
s
ar
ial
tr
ain
in
g
,
r
o
b
u
s
t
in
p
u
t
v
alid
atio
n
,
a
n
d
m
u
lti
-
lay
er
ed
a
u
th
en
ticatio
n
m
e
ch
an
is
m
s
s
h
o
u
ld
b
e
in
co
r
p
o
r
ated
to
s
af
e
g
u
ar
d
ag
ai
n
s
t p
o
ten
tial secu
r
ity
th
r
ea
ts
,
d
ata
p
o
is
o
n
in
g
,
o
r
u
n
au
t
h
o
r
ized
ac
ce
s
s
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
au
th
o
r
s
s
tate
th
at
n
o
f
u
n
d
i
n
g
was in
v
o
lv
e
d
in
s
u
p
p
o
r
tin
g
th
e
r
esear
ch
d
escr
ib
e
d
in
th
is
ar
ticle.
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